Text Generation
Transformers
Safetensors
qwen2
Generated from Trainer
rl-swarm
grpo
gensyn
I am reptilian strong gull
trl
genrl-swarm
I am reptilian_strong_gull
conversational
text-generation-inference
Instructions to use MajorJalud/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-reptilian_strong_gull with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MajorJalud/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-reptilian_strong_gull with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MajorJalud/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-reptilian_strong_gull") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MajorJalud/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-reptilian_strong_gull") model = AutoModelForCausalLM.from_pretrained("MajorJalud/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-reptilian_strong_gull", 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 MajorJalud/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-reptilian_strong_gull with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MajorJalud/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-reptilian_strong_gull" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MajorJalud/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-reptilian_strong_gull", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/MajorJalud/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-reptilian_strong_gull
- SGLang
How to use MajorJalud/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-reptilian_strong_gull 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 "MajorJalud/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-reptilian_strong_gull" \ --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": "MajorJalud/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-reptilian_strong_gull", "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 "MajorJalud/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-reptilian_strong_gull" \ --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": "MajorJalud/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-reptilian_strong_gull", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use MajorJalud/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-reptilian_strong_gull with Docker Model Runner:
docker model run hf.co/MajorJalud/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-reptilian_strong_gull
End of training
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|
| 205 |
"total_flos": 0.0,
|
| 206 |
+
"train_loss": 4.377111076792062e-06,
|
| 207 |
+
"train_runtime": 498.382,
|
| 208 |
+
"train_samples_per_second": 0.642,
|
| 209 |
+
"train_steps_per_second": 0.04
|
| 210 |
}
|
| 211 |
],
|
| 212 |
"logging_steps": 2,
|
| 213 |
+
"max_steps": 20,
|
| 214 |
"num_input_tokens_seen": 0,
|
| 215 |
+
"num_train_epochs": 3,
|
| 216 |
"save_steps": 25,
|
| 217 |
"stateful_callbacks": {
|
| 218 |
"TrainerControl": {
|
training_args.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 6008
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:7ce78aed34ce77353dad5b8d351f31cb61ddc17622dbf24fe538d806a99ae374
|
| 3 |
size 6008
|