Text Generation
Transformers
Safetensors
qwen2
Generated from Trainer
rl-swarm
grpo
gensyn
I am mimic singing hummingbird
unsloth
trl
genrl-swarm
I am mimic_singing_hummingbird
conversational
text-generation-inference
Instructions to use ESERCKR/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mimic_singing_hummingbird with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ESERCKR/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mimic_singing_hummingbird with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ESERCKR/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mimic_singing_hummingbird") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ESERCKR/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mimic_singing_hummingbird") model = AutoModelForCausalLM.from_pretrained("ESERCKR/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mimic_singing_hummingbird", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ESERCKR/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mimic_singing_hummingbird with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ESERCKR/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mimic_singing_hummingbird" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ESERCKR/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mimic_singing_hummingbird", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ESERCKR/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mimic_singing_hummingbird
- SGLang
How to use ESERCKR/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mimic_singing_hummingbird with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "ESERCKR/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mimic_singing_hummingbird" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ESERCKR/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mimic_singing_hummingbird", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "ESERCKR/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mimic_singing_hummingbird" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ESERCKR/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mimic_singing_hummingbird", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio
How to use ESERCKR/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mimic_singing_hummingbird with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for ESERCKR/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mimic_singing_hummingbird to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for ESERCKR/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mimic_singing_hummingbird to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ESERCKR/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mimic_singing_hummingbird to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="ESERCKR/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mimic_singing_hummingbird", max_seq_length=2048, ) - Docker Model Runner
How to use ESERCKR/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mimic_singing_hummingbird with Docker Model Runner:
docker model run hf.co/ESERCKR/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mimic_singing_hummingbird
End of training
Browse files- README.md +3 -4
- all_results.json +5 -5
- chat_template.jinja +54 -0
- config.json +1 -1
- generation_config.json +1 -1
- model.safetensors +1 -1
- tokenizer_config.json +0 -1
- train_results.json +5 -5
- trainer_state.json +102 -102
- training_args.bin +2 -2
README.md
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- grpo
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- gensyn
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- I am mimic singing hummingbird
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- unsloth
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- trl
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licence: license
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---
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### Framework versions
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- TRL: 0.15.2
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- Transformers: 4.
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- Pytorch: 2.
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- Datasets: 3.
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- Tokenizers: 0.21.1
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## Citations
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- grpo
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- gensyn
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- I am mimic singing hummingbird
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- trl
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licence: license
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---
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### Framework versions
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- TRL: 0.15.2
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- Transformers: 4.52.3
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- Pytorch: 2.5.1
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- Datasets: 3.6.0
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- Tokenizers: 0.21.1
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## Citations
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all_results.json
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{
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"total_flos": 0.0,
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"train_loss":
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"train_runtime":
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"train_samples":
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"train_steps_per_second": 0.
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}
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{
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"total_flos": 0.0,
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"train_loss": 0.07442864710465073,
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"train_runtime": 243.8844,
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"train_samples": 11,
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"train_samples_per_second": 1.312,
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"train_steps_per_second": 0.082
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}
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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config.json
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"sliding_window": 32768,
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"tie_word_embeddings": true,
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"torch_dtype": "float32",
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"transformers_version": "4.
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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"sliding_window": 32768,
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"tie_word_embeddings": true,
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"torch_dtype": "float32",
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"transformers_version": "4.52.3",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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generation_config.json
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"temperature": 0.7,
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"top_k": 20,
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"top_p": 0.8,
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"transformers_version": "4.
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}
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"temperature": 0.7,
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"top_k": 20,
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"top_p": 0.8,
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"transformers_version": "4.52.3"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 1976163472
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version https://git-lfs.github.com/spec/v1
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oid sha256:35e37f62117f3576f83d0ef12e9f697dfa06b8418fd6812c3761a380eabcbb6f
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size 1976163472
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tokenizer_config.json
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"<|video_pad|>"
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],
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"bos_token": null,
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-
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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"<|video_pad|>"
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],
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"bos_token": null,
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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train_results.json
CHANGED
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{
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"total_flos": 0.0,
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-
"train_loss":
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-
"train_runtime":
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-
"train_samples":
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"train_samples_per_second":
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-
"train_steps_per_second": 0.
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}
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{
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"total_flos": 0.0,
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+
"train_loss": 0.07442864710465073,
|
| 4 |
+
"train_runtime": 243.8844,
|
| 5 |
+
"train_samples": 11,
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| 6 |
+
"train_samples_per_second": 1.312,
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+
"train_steps_per_second": 0.082
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}
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trainer_state.json
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"best_global_step": null,
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"best_metric": null,
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"best_model_checkpoint": null,
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-
"epoch":
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"eval_steps": 500,
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"global_step": 20,
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"is_hyper_param_search": false,
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"is_world_process_zero": true,
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"log_history": [
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{
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-
"completion_length":
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-
"epoch": 0.
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-
"grad_norm":
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-
"kl": 0.
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"learning_rate": 5e-07,
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-
"loss": 0.0,
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-
"reward": 0.
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-
"reward_std": 0.
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"rewards/concensus_correctness_reward_func": 0.0,
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| 22 |
"rewards/consensus_reward_func": 0.0,
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| 23 |
"rewards/cumulative_reward_2": 0.0,
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| 24 |
-
"rewards/final_correctness_reward_func": 0.
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-
"rewards/question_recreation_reward_func": 0.
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"rewards/soft_format_reward_func": 0.0,
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"rewards/strict_format_reward_func": 0.0,
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| 28 |
-
"rewards/xmlcount_reward_func": 0.
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"step": 2
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},
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{
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-
"completion_length":
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-
"epoch": 1.
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-
"grad_norm":
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-
"kl": 0.
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| 36 |
"learning_rate": 4.864543104251586e-07,
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"loss": 0.0,
|
| 38 |
-
"reward": 0.
|
| 39 |
-
"reward_std": 0.
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| 40 |
"rewards/concensus_correctness_reward_func": 0.0,
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| 41 |
"rewards/consensus_reward_func": 0.0,
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"rewards/cumulative_reward_2": 0.0,
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| 43 |
-
"rewards/final_correctness_reward_func": 0.
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| 44 |
-
"rewards/question_recreation_reward_func": 0.
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| 45 |
"rewards/soft_format_reward_func": 0.0,
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| 46 |
"rewards/strict_format_reward_func": 0.0,
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| 47 |
-
"rewards/xmlcount_reward_func": 0.
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| 48 |
"step": 4
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| 49 |
},
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{
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| 193 |
"rewards/consensus_reward_func": 0.0,
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| 194 |
"rewards/cumulative_reward_2": 0.0,
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| 195 |
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"rewards/final_correctness_reward_func": 0.0,
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| 196 |
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"rewards/question_recreation_reward_func": 0.12451316439546645,
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| 197 |
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| 198 |
"rewards/strict_format_reward_func": 0.0,
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| 199 |
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"rewards/xmlcount_reward_func": -0.011406249832361937,
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| 200 |
"step": 20
|
| 201 |
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| 202 |
{
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| 203 |
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"epoch": 6.7272727272727275,
|
| 204 |
"step": 20,
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| 205 |
"total_flos": 0.0,
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| 206 |
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"train_loss": 0.07442864710465073,
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| 207 |
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"train_runtime": 243.8844,
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| 208 |
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"train_samples_per_second": 1.312,
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| 209 |
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"train_steps_per_second": 0.082
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| 210 |
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|
| 211 |
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| 212 |
"logging_steps": 2,
|
training_args.bin
CHANGED
|
@@ -1,3 +1,3 @@
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|
| 1 |
version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:
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| 3 |
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size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:ee2e01a2a185fb72cee67e9f8e46442e32f6a8457e6040783ea32a59be69f92f
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| 3 |
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size 5944
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