Text Generation
Transformers
TensorBoard
Safetensors
llama
llama-factory
full
Generated from Trainer
conversational
text-generation-inference
Instructions to use izzcw/final_combined_mc_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use izzcw/final_combined_mc_data with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="izzcw/final_combined_mc_data") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("izzcw/final_combined_mc_data") model = AutoModelForCausalLM.from_pretrained("izzcw/final_combined_mc_data") 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]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use izzcw/final_combined_mc_data with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "izzcw/final_combined_mc_data" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "izzcw/final_combined_mc_data", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/izzcw/final_combined_mc_data
- SGLang
How to use izzcw/final_combined_mc_data 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 "izzcw/final_combined_mc_data" \ --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": "izzcw/final_combined_mc_data", "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 "izzcw/final_combined_mc_data" \ --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": "izzcw/final_combined_mc_data", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use izzcw/final_combined_mc_data with Docker Model Runner:
docker model run hf.co/izzcw/final_combined_mc_data
End of training
Browse files- README.md +3 -2
- all_results.json +12 -0
- eval_results.json +7 -0
- runs/Mar25_06-37-26_cab5dc79-97e2-4af7-8451-ddd879bff597-0/events.out.tfevents.1742886915.cab5dc79-97e2-4af7-8451-ddd879bff597-0.6613.1 +3 -0
- train_results.json +8 -0
- trainer_state.json +106 -0
- training_eval_loss.png +0 -0
- training_loss.png +0 -0
README.md
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base_model: meta-llama/Meta-Llama-3-8B-Instruct
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tags:
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- llama-factory
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- generated_from_trainer
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model-index:
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- name: final_combined_mc_data
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# final_combined_mc_data
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This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the
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It achieves the following results on the evaluation set:
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- Loss: 0.
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## Model description
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base_model: meta-llama/Meta-Llama-3-8B-Instruct
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tags:
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- llama-factory
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- full
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- generated_from_trainer
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model-index:
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- name: final_combined_mc_data
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# final_combined_mc_data
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This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the identity and the final_combined_mc_data datasets.
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It achieves the following results on the evaluation set:
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- Loss: 0.8601
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## Model description
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all_results.json
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}
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eval_results.json
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}
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runs/Mar25_06-37-26_cab5dc79-97e2-4af7-8451-ddd879bff597-0/events.out.tfevents.1742886915.cab5dc79-97e2-4af7-8451-ddd879bff597-0.6613.1
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version https://git-lfs.github.com/spec/v1
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oid sha256:c786b30c7468f9d1032c56431bbe5d23328f675d90b5c8a21006085a9bdbcb9d
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size 306
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train_results.json
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trainer_state.json
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training_eval_loss.png
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training_loss.png
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