Text Generation
Transformers
Safetensors
English
blaze
causal-lm
custom-architecture
slm
small-language-model
chat
sft
instruction-tuned
conversational
custom_code
Instructions to use SurjoLabs/Blaze-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SurjoLabs/Blaze-SFT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SurjoLabs/Blaze-SFT", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("SurjoLabs/Blaze-SFT", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use SurjoLabs/Blaze-SFT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SurjoLabs/Blaze-SFT" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SurjoLabs/Blaze-SFT", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/SurjoLabs/Blaze-SFT
- SGLang
How to use SurjoLabs/Blaze-SFT 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 "SurjoLabs/Blaze-SFT" \ --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": "SurjoLabs/Blaze-SFT", "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 "SurjoLabs/Blaze-SFT" \ --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": "SurjoLabs/Blaze-SFT", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use SurjoLabs/Blaze-SFT with Docker Model Runner:
docker model run hf.co/SurjoLabs/Blaze-SFT
Download config.json from SurjoLabs/Blaze-SFT: direct link, hf CLI and curl.
- Browser
- Download file 1.06 kB
-
https://huggingface.co/SurjoLabs/Blaze-SFT/resolve/main/config.json
- Command line
-
hf download hf://SurjoLabs/Blaze-SFT/config.json
-
curl -L -o config.json https://huggingface.co/SurjoLabs/Blaze-SFT/resolve/main/config.json
1.06 kB
| { | |
| "architectures": [ | |
| "BlazeForCausalLM" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "auto_map": { | |
| "AutoConfig": "configuration_blaze.BlazeConfig", | |
| "AutoModelForCausalLM": "modeling_blaze.BlazeForCausalLM" | |
| }, | |
| "bos_token_id": 2, | |
| "coda_layers": 1, | |
| "dtype": "bfloat16", | |
| "eos_token_id": 6, | |
| "gradient_checkpointing": false, | |
| "head_dim": 64, | |
| "hidden_act": "silu", | |
| "hidden_size": 512, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 1536, | |
| "max_position_embeddings": 2048, | |
| "mlp_bias": false, | |
| "model_type": "blaze", | |
| "num_attention_heads": 8, | |
| "num_hidden_layers": 14, | |
| "num_key_value_heads": 4, | |
| "pad_token_id": 1, | |
| "prelude_layers": 1, | |
| "pretraining_tp": 1, | |
| "recurrent_layers": 12, | |
| "recurrent_passes": 2, | |
| "rms_norm_eps": 1e-05, | |
| "rope_parameters": { | |
| "rope_theta": 10000.0, | |
| "rope_type": "default" | |
| }, | |
| "rope_theta": 10000.0, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.16.1", | |
| "use_cache": true, | |
| "use_flash_attn": false, | |
| "vocab_size": 8192, | |
| "xsa_projection": true | |
| } | |