Text Generation
Transformers
Safetensors
gemma2
llama-factory
full
trl
dpo
Generated from Trainer
conversational
text-generation-inference
Instructions to use sedrickkeh/checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sedrickkeh/checkpoints with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="sedrickkeh/checkpoints") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("sedrickkeh/checkpoints") model = AutoModelForCausalLM.from_pretrained("sedrickkeh/checkpoints", 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]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use sedrickkeh/checkpoints with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sedrickkeh/checkpoints" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sedrickkeh/checkpoints", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/sedrickkeh/checkpoints
- SGLang
How to use sedrickkeh/checkpoints 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 "sedrickkeh/checkpoints" \ --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": "sedrickkeh/checkpoints", "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 "sedrickkeh/checkpoints" \ --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": "sedrickkeh/checkpoints", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use sedrickkeh/checkpoints with Docker Model Runner:
docker model run hf.co/sedrickkeh/checkpoints
End of training
Browse files- README.md +2 -1
- all_results.json +7 -7
- eval_results.json +3 -3
- train_results.json +4 -4
- trainer_state.json +16 -16
- training_loss.png +0 -0
README.md
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base_model: meta-llama/Meta-Llama-3-8B
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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: model
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# model
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This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on
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It achieves the following results on the evaluation set:
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- Loss: nan
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base_model: meta-llama/Meta-Llama-3-8B
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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: model
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# model
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This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the PrincetonPLI/Instruct-SkillMix-SDD dataset.
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It achieves the following results on the evaluation set:
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- Loss: nan
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all_results.json
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{
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"epoch": 2.4,
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"eval_loss": NaN,
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"eval_runtime": 2.
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"eval_samples_per_second":
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"total_flos": 4.249521302148219e+17,
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"train_loss": 0.
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"train_samples_per_second": 9.
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"train_steps_per_second": 0.
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{
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"epoch": 2.4,
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"eval_loss": NaN,
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"eval_runtime": 2.0791,
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"eval_samples_per_second": 48.58,
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"eval_steps_per_second": 0.962,
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"total_flos": 4.249521302148219e+17,
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"train_loss": 0.4152366585201687,
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"train_runtime": 603.4863,
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"train_samples_per_second": 9.54,
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"train_steps_per_second": 0.015
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}
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eval_results.json
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"epoch": 2.4,
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"epoch": 2.4,
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"eval_runtime": 2.0791,
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"eval_samples_per_second": 48.58,
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"eval_steps_per_second": 0.962
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train_results.json
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"epoch": 2.4,
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"train_loss": 0.4152366585201687,
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"train_runtime": 603.4863,
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"train_samples_per_second": 9.54,
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"train_steps_per_second": 0.015
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trainer_state.json
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"grad_norm":
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"logging_steps": 1.0,
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"log_history": [
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"grad_norm": 67.7900161743164,
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"learning_rate": 5e-06,
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"loss": 1.9082,
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"loss": 1.829,
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"eval_samples_per_second": 42.467,
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"eval_samples_per_second": 42.66,
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"total_flos": 4.249521302148219e+17,
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"logging_steps": 1.0,
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training_loss.png
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