Instructions to use microsoft/Dayhoff-170m-GR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use microsoft/Dayhoff-170m-GR with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="microsoft/Dayhoff-170m-GR")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("microsoft/Dayhoff-170m-GR")
model = AutoModelForCausalLM.from_pretrained("microsoft/Dayhoff-170m-GR")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use microsoft/Dayhoff-170m-GR with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "microsoft/Dayhoff-170m-GR"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "microsoft/Dayhoff-170m-GR",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/microsoft/Dayhoff-170m-GR

SGLang

How to use microsoft/Dayhoff-170m-GR 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 "microsoft/Dayhoff-170m-GR" \
    --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": "microsoft/Dayhoff-170m-GR",
		"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 "microsoft/Dayhoff-170m-GR" \
        --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": "microsoft/Dayhoff-170m-GR",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use microsoft/Dayhoff-170m-GR with Docker Model Runner:
```
docker model run hf.co/microsoft/Dayhoff-170m-GR
```

sarahalamdari commited on Jul 31, 2025

Commit

e4e786f

verified ·

1 Parent(s): c3ec161

Update config.json

Browse files

Files changed (1) hide show

config.json +0 -7

config.json CHANGED Viewed

@@ -1,17 +1,10 @@
 {
-  "_name_or_path": "ai21labs/Jamba-v0.1",
   "architectures": [
     "JambaForCausalLM"
   ],
   "attention_dropout": 0.0,
   "attn_layer_offset": 4,
   "attn_layer_period": 8,
-  "auto_map": {
-    "AutoConfig": "ai21labs/Jamba-v0.1--configuration_jamba.JambaConfig",
-    "AutoModel": "ai21labs/Jamba-v0.1--modeling_jamba.JambaModel",
-    "AutoModelForCausalLM": "ai21labs/Jamba-v0.1--modeling_jamba.JambaForCausalLM",
-    "AutoModelForSequenceClassification": "ai21labs/Jamba-v0.1--model.JambaForSequenceClassification"
-  },
   "bos_token_id": 29,
   "eos_token_id": 27,
   "expert_layer_offset": 1,

 {
   "architectures": [
     "JambaForCausalLM"
   ],
   "attention_dropout": 0.0,
   "attn_layer_offset": 4,
   "attn_layer_period": 8,
   "bos_token_id": 29,
   "eos_token_id": 27,
   "expert_layer_offset": 1,