Instructions to use dphn/dolphin-2.9.1-qwen-110b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dphn/dolphin-2.9.1-qwen-110b with Transformers:

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

pipe = pipeline("text-generation", model="dphn/dolphin-2.9.1-qwen-110b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dphn/dolphin-2.9.1-qwen-110b")
model = AutoModelForCausalLM.from_pretrained("dphn/dolphin-2.9.1-qwen-110b")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dphn/dolphin-2.9.1-qwen-110b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dphn/dolphin-2.9.1-qwen-110b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dphn/dolphin-2.9.1-qwen-110b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dphn/dolphin-2.9.1-qwen-110b

SGLang

How to use dphn/dolphin-2.9.1-qwen-110b 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 "dphn/dolphin-2.9.1-qwen-110b" \
    --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": "dphn/dolphin-2.9.1-qwen-110b",
		"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 "dphn/dolphin-2.9.1-qwen-110b" \
        --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": "dphn/dolphin-2.9.1-qwen-110b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dphn/dolphin-2.9.1-qwen-110b with Docker Model Runner:
```
docker model run hf.co/dphn/dolphin-2.9.1-qwen-110b
```

ehartford commited on May 12, 2024

Commit

3dc4484

verified ·

1 Parent(s): b9fd220

Update README.md

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@@ -1,13 +1,59 @@
 ---
 tags:
 - generated_from_trainer
-model-index:
-- name: qwen-out
-  results: []
 ---
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
 [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
 <details><summary>See axolotl config</summary>
@@ -376,24 +422,6 @@ special_tokens:
 </details><br>
-# qwen-out
-This model was trained from scratch on the None dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.3931
-## Model description
-More information needed
-## Intended uses & limitations
-More information needed
-## Training and evaluation data
-More information needed
 ## Training procedure
 ### Training hyperparameters

 ---
+license: other
+base_model: Qwen/Qwen1.5-110B
 tags:
 - generated_from_trainer
+- axolotl
+datasets:
+- cognitivecomputations/Dolphin-2.9
+- teknium/OpenHermes-2.5
+- m-a-p/CodeFeedback-Filtered-Instruction
+- cognitivecomputations/dolphin-coder
+- cognitivecomputations/samantha-data
+- microsoft/orca-math-word-problems-200k
+- Locutusque/function-calling-chatml
+- internlm/Agent-FLAN
 ---
+# Dolphin 2.9.1 Qwen 110b 🐬
+Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations
+Discord: https://discord.gg/8fbBeC7ZGx
+<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" />
+Our appreciation for the sponsors of Dolphin 2.9.1:
+- [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 8xH100 node
+This model is based on Qwen1.5-110B, and is governed by [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](LICENSE)
+The base model has 32k context, and the full-weight fine-tuning was with 8k sequence length.
+This model was trained FFT on parameters selected by [Laser Scanner](https://github.com/cognitivecomputations/laserRMT/blob/main/laser_scanner.py), using ChatML prompt template format.
+example:
+```
+<|im_start|>system
+You are Dolphin, a helpful AI assistant.<|im_end|>
+<|im_start|>user
+{prompt}<|im_end|>
+<|im_start|>assistant
+```
+Dolphin-2.9.1 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.
+Dolphin is uncensored. We have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly.
+Dolphin is licensed according to Meta's Llama license.  We grant permission for any use, including commercial, that falls within accordance with Meta's Llama-3 license.  Dolphin was trained on data generated from GPT4, among other models.
+## Evals
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/U86Zu-MzLq4rECJRAAvgq.png)
+## Training
 [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
 <details><summary>See axolotl config</summary>
 </details><br>
 ## Training procedure
 ### Training hyperparameters