Text Generation
Transformers
Safetensors
English
llama
text-generation-inference
unsloth
trl
sft
conversational
Instructions to use Elcaida/pretrained2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Elcaida/pretrained2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Elcaida/pretrained2") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Elcaida/pretrained2") model = AutoModelForCausalLM.from_pretrained("Elcaida/pretrained2", 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 Elcaida/pretrained2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Elcaida/pretrained2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Elcaida/pretrained2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Elcaida/pretrained2
- SGLang
How to use Elcaida/pretrained2 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 "Elcaida/pretrained2" \ --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": "Elcaida/pretrained2", "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 "Elcaida/pretrained2" \ --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": "Elcaida/pretrained2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Desktop
- Docker Model Runner
How to use Elcaida/pretrained2 with Docker Model Runner:
docker model run hf.co/Elcaida/pretrained2
Upload model trained with Unsloth
Browse filesUpload model trained with Unsloth 2x faster
- README.md +1 -0
- config.json +38 -0
- generation_config.json +14 -0
- model.safetensors +3 -0
README.md
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- unsloth
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- llama
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- trl
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license: apache-2.0
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language:
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- en
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- unsloth
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- llama
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- trl
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- sft
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license: apache-2.0
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language:
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- en
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"eos_token_id": 128009,
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"head_dim": 64,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 8192,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 16,
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"num_key_value_heads": 8,
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"pad_token_id": 128004,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
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"factor": 32.0,
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"high_freq_factor": 4.0,
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"low_freq_factor": 1.0,
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"original_max_position_embeddings": 8192,
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"rope_type": "llama3"
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},
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"rope_theta": 500000.0,
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"tie_word_embeddings": true,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.50.0.dev0",
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"unsloth_fixed": true,
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"unsloth_version": "2025.3.19",
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"use_cache": true,
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"vocab_size": 128256
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}
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generation_config.json
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{
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"bos_token_id": 128000,
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"do_sample": true,
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"eos_token_id": [
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128001,
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128008,
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128009
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],
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"max_length": 131072,
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"pad_token_id": 128004,
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"temperature": 0.6,
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"top_p": 0.9,
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"transformers_version": "4.50.0.dev0"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:9f9124cc2ec1469f87f34fdd94c0a0cc6fc0f93bd9ae3793d4354679f812beac
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size 2471645608
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