Instructions to use InxiteOut/bloom560m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use InxiteOut/bloom560m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="InxiteOut/bloom560m")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("InxiteOut/bloom560m") model = AutoModelForCausalLM.from_pretrained("InxiteOut/bloom560m") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use InxiteOut/bloom560m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "InxiteOut/bloom560m" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "InxiteOut/bloom560m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/InxiteOut/bloom560m
- SGLang
How to use InxiteOut/bloom560m 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 "InxiteOut/bloom560m" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "InxiteOut/bloom560m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "InxiteOut/bloom560m" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "InxiteOut/bloom560m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use InxiteOut/bloom560m with Docker Model Runner:
docker model run hf.co/InxiteOut/bloom560m
Upload BloomForCausalLM
Browse files- config.json +1 -13
- pytorch_model.bin +2 -2
config.json
CHANGED
|
@@ -21,22 +21,10 @@
|
|
| 21 |
"offset_alibi": 100,
|
| 22 |
"pad_token_id": 3,
|
| 23 |
"pretraining_tp": 1,
|
| 24 |
-
"quantization_config": {
|
| 25 |
-
"bnb_4bit_compute_dtype": "float32",
|
| 26 |
-
"bnb_4bit_quant_type": "fp4",
|
| 27 |
-
"bnb_4bit_use_double_quant": false,
|
| 28 |
-
"llm_int8_enable_fp32_cpu_offload": false,
|
| 29 |
-
"llm_int8_has_fp16_weight": false,
|
| 30 |
-
"llm_int8_skip_modules": null,
|
| 31 |
-
"llm_int8_threshold": 6.0,
|
| 32 |
-
"load_in_4bit": false,
|
| 33 |
-
"load_in_8bit": true,
|
| 34 |
-
"quant_method": "bitsandbytes"
|
| 35 |
-
},
|
| 36 |
"skip_bias_add": true,
|
| 37 |
"skip_bias_add_qkv": false,
|
| 38 |
"slow_but_exact": false,
|
| 39 |
-
"torch_dtype": "
|
| 40 |
"transformers_version": "4.34.0.dev0",
|
| 41 |
"unk_token_id": 0,
|
| 42 |
"use_cache": true,
|
|
|
|
| 21 |
"offset_alibi": 100,
|
| 22 |
"pad_token_id": 3,
|
| 23 |
"pretraining_tp": 1,
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 24 |
"skip_bias_add": true,
|
| 25 |
"skip_bias_add_qkv": false,
|
| 26 |
"slow_but_exact": false,
|
| 27 |
+
"torch_dtype": "float32",
|
| 28 |
"transformers_version": "4.34.0.dev0",
|
| 29 |
"unk_token_id": 0,
|
| 30 |
"use_cache": true,
|
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:53d6c11ac275f00176b10713c6248bcd91bf1b155912e8630aaa6b946915411e
|
| 3 |
+
size 2236957537
|