Text Generation
PEFT
Safetensors
Transformers
grpo
lora
trl
unsloth
swe-gym
code-repair
conversational
Instructions to use imdatta0/qwen3-4b-swegym-moto-kl02-multihint-grpo-b02-lr2e6-s25-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use imdatta0/qwen3-4b-swegym-moto-kl02-multihint-grpo-b02-lr2e6-s25-adapter with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/mnt/disks/unslothai/datta0/.cache/hub/models--unsloth--Qwen3-4B-Instruct-2507/snapshots/992063681dc2f7de4ee976110199552935cad284") model = PeftModel.from_pretrained(base_model, "imdatta0/qwen3-4b-swegym-moto-kl02-multihint-grpo-b02-lr2e6-s25-adapter") - Transformers
How to use imdatta0/qwen3-4b-swegym-moto-kl02-multihint-grpo-b02-lr2e6-s25-adapter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="imdatta0/qwen3-4b-swegym-moto-kl02-multihint-grpo-b02-lr2e6-s25-adapter") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("imdatta0/qwen3-4b-swegym-moto-kl02-multihint-grpo-b02-lr2e6-s25-adapter", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use imdatta0/qwen3-4b-swegym-moto-kl02-multihint-grpo-b02-lr2e6-s25-adapter with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "imdatta0/qwen3-4b-swegym-moto-kl02-multihint-grpo-b02-lr2e6-s25-adapter" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "imdatta0/qwen3-4b-swegym-moto-kl02-multihint-grpo-b02-lr2e6-s25-adapter", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/imdatta0/qwen3-4b-swegym-moto-kl02-multihint-grpo-b02-lr2e6-s25-adapter
- SGLang
How to use imdatta0/qwen3-4b-swegym-moto-kl02-multihint-grpo-b02-lr2e6-s25-adapter 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 "imdatta0/qwen3-4b-swegym-moto-kl02-multihint-grpo-b02-lr2e6-s25-adapter" \ --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": "imdatta0/qwen3-4b-swegym-moto-kl02-multihint-grpo-b02-lr2e6-s25-adapter", "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 "imdatta0/qwen3-4b-swegym-moto-kl02-multihint-grpo-b02-lr2e6-s25-adapter" \ --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": "imdatta0/qwen3-4b-swegym-moto-kl02-multihint-grpo-b02-lr2e6-s25-adapter", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Desktop
- Docker Model Runner
How to use imdatta0/qwen3-4b-swegym-moto-kl02-multihint-grpo-b02-lr2e6-s25-adapter with Docker Model Runner:
docker model run hf.co/imdatta0/qwen3-4b-swegym-moto-kl02-multihint-grpo-b02-lr2e6-s25-adapter
| {%- if tools %} | |
| {{- '<|im_start|>system\n' }} | |
| {%- if messages[0].role == 'system' %} | |
| {{- messages[0].content + '\n\n' }} | |
| {%- endif %} | |
| {{- "# 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>" }} | |
| {%- for tool in tools %} | |
| {{- "\n" }} | |
| {{- tool | tojson }} | |
| {%- endfor %} | |
| {{- "\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" }} | |
| {%- else %} | |
| {%- if messages[0].role == 'system' %} | |
| {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }} | |
| {%- endif %} | |
| {%- endif %} | |
| {%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %} | |
| {%- for message in messages[::-1] %} | |
| {%- set index = (messages|length - 1) - loop.index0 %} | |
| {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %} | |
| {%- set ns.multi_step_tool = false %} | |
| {%- set ns.last_query_index = index %} | |
| {%- endif %} | |
| {%- endfor %} | |
| {%- for message in messages %} | |
| {%- if message.content is string %} | |
| {%- set content = message.content %} | |
| {%- else %} | |
| {%- set content = '' %} | |
| {%- endif %} | |
| {%- if (message.role == "user") or (message.role == "system" and not loop.first) %} | |
| {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }} | |
| {%- elif message.role == "assistant" %} | |
| {%- set reasoning_content = '' %} | |
| {%- if message.reasoning_content is string %} | |
| {%- set reasoning_content = message.reasoning_content %} | |
| {%- else %} | |
| {%- if '</think>' in content %} | |
| {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %} | |
| {%- set content = content.split('</think>')[-1].lstrip('\n') %} | |
| {%- endif %} | |
| {%- endif %} | |
| {%- if loop.index0 > ns.last_query_index %} | |
| {%- if loop.last or (not loop.last and reasoning_content) %} | |
| {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }} | |
| {%- else %} | |
| {{- '<|im_start|>' + message.role + '\n' + content }} | |
| {%- endif %} | |
| {%- else %} | |
| {{- '<|im_start|>' + message.role + '\n' + content }} | |
| {%- endif %} | |
| {%- if message.tool_calls %} | |
| {%- for tool_call in message.tool_calls %} | |
| {%- if (loop.first and content) or (not loop.first) %} | |
| {{- '\n' }} | |
| {%- endif %} | |
| {%- if tool_call.function %} | |
| {%- set tool_call = tool_call.function %} | |
| {%- endif %} | |
| {{- '<tool_call>\n{"name": "' }} | |
| {{- tool_call.name }} | |
| {{- '", "arguments": ' }} | |
| {%- if tool_call.arguments is string %} | |
| {{- tool_call.arguments }} | |
| {%- else %} | |
| {{- tool_call.arguments | tojson }} | |
| {%- endif %} | |
| {{- '}\n</tool_call>' }} | |
| {%- endfor %} | |
| {%- endif %} | |
| {{- '<|im_end|>\n' }} | |
| {%- elif message.role == "tool" %} | |
| {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %} | |
| {{- '<|im_start|>user' }} | |
| {%- endif %} | |
| {{- '\n<tool_response>\n' }} | |
| {{- content }} | |
| {{- '\n</tool_response>' }} | |
| {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %} | |
| {{- '<|im_end|>\n' }} | |
| {%- endif %} | |
| {%- endif %} | |
| {%- endfor %} | |
| {%- if add_generation_prompt %} | |
| {{- '<|im_start|>assistant\n' }} | |
| {%- endif %} |