Text Generation
Transformers
Safetensors
qwen3
llama-factory
full
Generated from Trainer
conversational
text-generation-inference
Instructions to use Jashan887/65_Bash_3K_Traces with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jashan887/65_Bash_3K_Traces with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Jashan887/65_Bash_3K_Traces") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Jashan887/65_Bash_3K_Traces") model = AutoModelForCausalLM.from_pretrained("Jashan887/65_Bash_3K_Traces") 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 Jashan887/65_Bash_3K_Traces with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Jashan887/65_Bash_3K_Traces" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Jashan887/65_Bash_3K_Traces", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Jashan887/65_Bash_3K_Traces
- SGLang
How to use Jashan887/65_Bash_3K_Traces 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 "Jashan887/65_Bash_3K_Traces" \ --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": "Jashan887/65_Bash_3K_Traces", "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 "Jashan887/65_Bash_3K_Traces" \ --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": "Jashan887/65_Bash_3K_Traces", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Jashan887/65_Bash_3K_Traces with Docker Model Runner:
docker model run hf.co/Jashan887/65_Bash_3K_Traces
| { | |
| "achieved_tflops_per_gpu": 0.001951399015158965, | |
| "achieved_tflops_per_gpu_theoretical": 1016.3713688469504, | |
| "epoch": 6.0, | |
| "loss_nan_ranks": 0, | |
| "loss_rank_avg": 0.133074551820755, | |
| "mfu_percent": 0.00013790805760840742, | |
| "mfu_percent_theoretical": 71.82836528953713, | |
| "total_flos": 123574112944128.0, | |
| "train_loss": 0.2030692549064906, | |
| "train_runtime": 3957.8692, | |
| "train_samples_per_second": 4.79, | |
| "train_steps_per_second": 0.3, | |
| "valid_targets_mean": 1319.8, | |
| "valid_targets_min": 875 | |
| } |