Instructions to use philschmid/instruct-igel-001 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use philschmid/instruct-igel-001 with Transformers:

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

pipe = pipeline("text-generation", model="philschmid/instruct-igel-001")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("philschmid/instruct-igel-001")
model = AutoModelForCausalLM.from_pretrained("philschmid/instruct-igel-001")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use philschmid/instruct-igel-001 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "philschmid/instruct-igel-001"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "philschmid/instruct-igel-001",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/philschmid/instruct-igel-001

SGLang

How to use philschmid/instruct-igel-001 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 "philschmid/instruct-igel-001" \
    --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": "philschmid/instruct-igel-001",
		"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 "philschmid/instruct-igel-001" \
        --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": "philschmid/instruct-igel-001",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use philschmid/instruct-igel-001 with Docker Model Runner:
```
docker model run hf.co/philschmid/instruct-igel-001
```

philschmid commited on Apr 3, 2023

Commit

3e689a4

1 Parent(s): 60076d5

Upload 2 files

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Files changed (2) hide show

adapter_config.json +21 -0
adapter_model.bin +3 -0

adapter_config.json ADDED Viewed

	@@ -0,0 +1,21 @@

+{
+  "base_model_name_or_path": "malteos/bloom-6b4-clp-german",
+  "bias": "none",
+  "enable_lora": [
+    true,
+    false,
+    true
+  ],
+  "fan_in_fan_out": true,
+  "inference_mode": true,
+  "lora_alpha": 32,
+  "lora_dropout": 0.05,
+  "merge_weights": false,
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 8,
+  "target_modules": [
+    "query_key_value"
+  ],
+  "task_type": "CAUSAL_LM"
+}

adapter_model.bin ADDED Viewed

	@@ -0,0 +1,3 @@

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+oid sha256:ae906d2ee09ce67149aa63d1270671b6163440188327e2af52204962882a1007
+size 15751077