Text Generation
Transformers
Safetensors
llama
Generated from Trainer
open-r1
trl
grpo
conversational
text-generation-inference
Instructions to use zerowsw/coderm-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zerowsw/coderm-8b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="zerowsw/coderm-8b") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("zerowsw/coderm-8b") model = AutoModelForCausalLM.from_pretrained("zerowsw/coderm-8b", 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use zerowsw/coderm-8b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "zerowsw/coderm-8b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zerowsw/coderm-8b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/zerowsw/coderm-8b
- SGLang
How to use zerowsw/coderm-8b 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 "zerowsw/coderm-8b" \ --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": "zerowsw/coderm-8b", "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 "zerowsw/coderm-8b" \ --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": "zerowsw/coderm-8b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use zerowsw/coderm-8b with Docker Model Runner:
docker model run hf.co/zerowsw/coderm-8b
Model save
Browse files- README.md +1 -3
- adapter_config.json +5 -5
- adapter_model.safetensors +1 -1
- all_results.json +4 -4
- tokenizer.json +2 -2
- train_results.json +4 -4
- trainer_state.json +11 -533
- training_args.bin +1 -1
README.md
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---
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base_model: KAKA22/CodeRM-8B
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datasets: zzzzit/taco3
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library_name: transformers
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model_name: coderm-8b
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tags:
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- generated_from_trainer
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- open-r1
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- trl
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- grpo
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licence: license
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# Model Card for coderm-8b
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This model is a fine-tuned version of [KAKA22/CodeRM-8B](https://huggingface.co/KAKA22/CodeRM-8B)
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It has been trained using [TRL](https://github.com/huggingface/trl).
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## Quick start
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---
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base_model: KAKA22/CodeRM-8B
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library_name: transformers
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model_name: coderm-8b
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tags:
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- generated_from_trainer
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- trl
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- grpo
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licence: license
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# Model Card for coderm-8b
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This model is a fine-tuned version of [KAKA22/CodeRM-8B](https://huggingface.co/KAKA22/CodeRM-8B).
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It has been trained using [TRL](https://github.com/huggingface/trl).
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## Quick start
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adapter_config.json
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training_args.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
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oid sha256:
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