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
```

dolphin-2.9.1-qwen-110b / README.md

ehartford

Update README.md

3dc4484 verified about 2 years ago

preview code

Raw

History Blame

14.7 kB

	---
	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>

	axolotl version: `0.4.0`
	```yaml
	base_model: /workspace/axolotl/qwen-checkpoint
	model_type: AutoModelForCausalLM
	tokenizer_type: AutoTokenizer

	# trust_remote_code: true

	# load_in_8bit: true
	# load_in_4bit: true
	# strict: false

	datasets:
	- path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.jsonl
	type: sharegpt
	conversation: chatml
	# - path: /workspace/datasets/dolphin-2.9/Ultrachat200kunfiltered.jsonl
	# type: sharegpt
	# conversation: chatml
	- path: /workspace/datasets/dolphin-2.9/dolphin-coder-translate-sharegpt2.jsonl
	type: sharegpt
	conversation: chatml
	- path: /workspace/datasets/dolphin-2.9/dolphin-coder-codegen-sharegpt2.jsonl
	type: sharegpt
	conversation: chatml
	- path: /workspace/datasets/dolphin-2.9/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl
	type: sharegpt
	conversation: chatml
	- path: /workspace/datasets/dolphin-2.9/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl
	type: sharegpt
	conversation: chatml
	- path: /workspace/datasets/dolphin-2.9/not_samantha_norefusals.jsonl
	type: sharegpt
	conversation: chatml
	- path: /workspace/datasets/dolphin-2.9/Orca-Math-resort-unfiltered.jsonl
	type: sharegpt
	conversation: chatml
	- path: /workspace/datasets/dolphin-2.9/agent_instruct_react_unfiltered.jsonl
	type: sharegpt
	conversation: chatml
	- path: /workspace/datasets/dolphin-2.9/toolbench_instruct_j1s1_3k_unfiltered.jsonl
	type: sharegpt
	conversation: chatml
	- path: /workspace/datasets/dolphin-2.9/toolbench_negative_unfiltered.jsonl
	type: sharegpt
	conversation: chatml
	- path: /workspace/datasets/dolphin-2.9/toolbench_react_10p_unfiltered.jsonl
	type: sharegpt
	conversation: chatml
	- path: /workspace/datasets/dolphin-2.9/toolbench_tflan_cot_30p_unfiltered.jsonl
	type: sharegpt
	conversation: chatml
	- path: /workspace/datasets/dolphin-2.9/openhermes200k_unfiltered.jsonl
	type: sharegpt
	conversation: chatml
	# - path: /workspace/datasets/dolphin-2.9/SystemConversations.jsonl
	# type: sharegpt
	# conversation: chatml

	chat_template: chatml
	dataset_prepared_path: last_run_prepared
	val_set_size: 0.01
	output_dir: ./qwen-out

	# adapter: qlora
	# lora_r: 16
	# lora_alpha: 16
	# lora_modules_to_save: [embed_tokens, lm_head]
	# lora_dropout: 0.05
	# lora_target_linear: false

	unfrozen_parameters:
	- ^lm_head.weight$
	- ^model.embed_tokens.weight$
	# input_layernorm layers
	- model.layers.0.input_layernorm
	- model.layers.1.input_layernorm
	- model.layers.2.input_layernorm
	- model.layers.3.input_layernorm
	- model.layers.4.input_layernorm
	- model.layers.5.input_layernorm
	- model.layers.6.input_layernorm
	- model.layers.7.input_layernorm
	- model.layers.8.input_layernorm
	- model.layers.9.input_layernorm
	- model.layers.10.input_layernorm
	- model.layers.11.input_layernorm
	- model.layers.12.input_layernorm
	- model.layers.13.input_layernorm
	- model.layers.14.input_layernorm
	- model.layers.15.input_layernorm
	- model.layers.16.input_layernorm
	- model.layers.17.input_layernorm
	- model.layers.18.input_layernorm
	- model.layers.19.input_layernorm
	- model.layers.20.input_layernorm
	- model.layers.21.input_layernorm
	- model.layers.22.input_layernorm
	- model.layers.23.input_layernorm
	# lm_head layers
	# mlp.down_proj layers
	- model.layers.17.mlp.down_proj
	- model.layers.18.mlp.down_proj
	- model.layers.19.mlp.down_proj
	- model.layers.20.mlp.down_proj
	- model.layers.21.mlp.down_proj
	- model.layers.22.mlp.down_proj
	- model.layers.23.mlp.down_proj
	- model.layers.24.mlp.down_proj
	- model.layers.25.mlp.down_proj
	- model.layers.26.mlp.down_proj
	- model.layers.27.mlp.down_proj
	- model.layers.28.mlp.down_proj
	- model.layers.29.mlp.down_proj
	- model.layers.30.mlp.down_proj
	- model.layers.31.mlp.down_proj
	- model.layers.32.mlp.down_proj
	- model.layers.33.mlp.down_proj
	- model.layers.34.mlp.down_proj
	- model.layers.35.mlp.down_proj
	- model.layers.36.mlp.down_proj
	- model.layers.37.mlp.down_proj
	- model.layers.38.mlp.down_proj
	- model.layers.39.mlp.down_proj
	- model.layers.40.mlp.down_proj
	# mlp.gate_proj layers
	- model.layers.51.mlp.gate_proj
	- model.layers.50.mlp.gate_proj
	- model.layers.53.mlp.gate_proj
	- model.layers.52.mlp.gate_proj
	- model.layers.49.mlp.gate_proj
	- model.layers.45.mlp.gate_proj
	- model.layers.46.mlp.gate_proj
	- model.layers.47.mlp.gate_proj
	- model.layers.57.mlp.gate_proj
	- model.layers.48.mlp.gate_proj
	- model.layers.56.mlp.gate_proj
	- model.layers.41.mlp.gate_proj
	- model.layers.54.mlp.gate_proj
	- model.layers.43.mlp.gate_proj
	- model.layers.44.mlp.gate_proj
	- model.layers.60.mlp.gate_proj
	- model.layers.55.mlp.gate_proj
	- model.layers.40.mlp.gate_proj
	- model.layers.42.mlp.gate_proj
	- model.layers.58.mlp.gate_proj
	- model.layers.36.mlp.gate_proj
	- model.layers.37.mlp.gate_proj
	- model.layers.38.mlp.gate_proj
	- model.layers.39.mlp.gate_proj
	# mlp.up_proj layers
	- model.layers.50.mlp.up_proj
	- model.layers.51.mlp.up_proj
	- model.layers.41.mlp.up_proj
	- model.layers.49.mlp.up_proj
	- model.layers.43.mlp.up_proj
	- model.layers.44.mlp.up_proj
	- model.layers.40.mlp.up_proj
	- model.layers.45.mlp.up_proj
	- model.layers.47.mlp.up_proj
	- model.layers.48.mlp.up_proj
	- model.layers.46.mlp.up_proj
	- model.layers.42.mlp.up_proj
	- model.layers.39.mlp.up_proj
	- model.layers.36.mlp.up_proj
	- model.layers.37.mlp.up_proj
	- model.layers.38.mlp.up_proj
	- model.layers.56.mlp.up_proj
	- model.layers.57.mlp.up_proj
	- model.layers.53.mlp.up_proj
	- model.layers.31.mlp.up_proj
	- model.layers.32.mlp.up_proj
	- model.layers.34.mlp.up_proj
	- model.layers.35.mlp.up_proj
	- model.layers.33.mlp.up_proj
	# model.embed_tokens layers
	# model.norm layers
	# post_attention_layernorm layers
	- model.layers.0.post_attention_layernorm
	- model.layers.1.post_attention_layernorm
	- model.layers.2.post_attention_layernorm
	- model.layers.3.post_attention_layernorm
	- model.layers.4.post_attention_layernorm
	- model.layers.5.post_attention_layernorm
	- model.layers.6.post_attention_layernorm
	- model.layers.7.post_attention_layernorm
	- model.layers.8.post_attention_layernorm
	- model.layers.9.post_attention_layernorm
	- model.layers.10.post_attention_layernorm
	- model.layers.11.post_attention_layernorm
	- model.layers.12.post_attention_layernorm
	- model.layers.13.post_attention_layernorm
	- model.layers.14.post_attention_layernorm
	- model.layers.15.post_attention_layernorm
	- model.layers.16.post_attention_layernorm
	- model.layers.17.post_attention_layernorm
	- model.layers.18.post_attention_layernorm
	- model.layers.19.post_attention_layernorm
	- model.layers.20.post_attention_layernorm
	- model.layers.21.post_attention_layernorm
	- model.layers.22.post_attention_layernorm
	- model.layers.23.post_attention_layernorm
	# self_attn.k_proj layers
	- model.layers.42.self_attn.k_proj
	- model.layers.41.self_attn.k_proj
	- model.layers.39.self_attn.k_proj
	- model.layers.35.self_attn.k_proj
	- model.layers.28.self_attn.k_proj
	- model.layers.79.self_attn.k_proj
	- model.layers.43.self_attn.k_proj
	- model.layers.32.self_attn.k_proj
	- model.layers.73.self_attn.k_proj
	- model.layers.31.self_attn.k_proj
	- model.layers.29.self_attn.k_proj
	- model.layers.76.self_attn.k_proj
	- model.layers.30.self_attn.k_proj
	- model.layers.40.self_attn.k_proj
	- model.layers.33.self_attn.k_proj
	- model.layers.78.self_attn.k_proj
	- model.layers.34.self_attn.k_proj
	- model.layers.37.self_attn.k_proj
	- model.layers.45.self_attn.k_proj
	- model.layers.44.self_attn.k_proj
	- model.layers.71.self_attn.k_proj
	- model.layers.26.self_attn.k_proj
	- model.layers.74.self_attn.k_proj
	- model.layers.27.self_attn.k_proj
	# self_attn.o_proj layers
	- model.layers.35.self_attn.o_proj
	- model.layers.34.self_attn.o_proj
	- model.layers.37.self_attn.o_proj
	- model.layers.33.self_attn.o_proj
	- model.layers.31.self_attn.o_proj
	- model.layers.27.self_attn.o_proj
	- model.layers.38.self_attn.o_proj
	- model.layers.24.self_attn.o_proj
	- model.layers.39.self_attn.o_proj
	- model.layers.43.self_attn.o_proj
	- model.layers.29.self_attn.o_proj
	- model.layers.0.self_attn.o_proj
	- model.layers.50.self_attn.o_proj
	- model.layers.32.self_attn.o_proj
	- model.layers.45.self_attn.o_proj
	- model.layers.30.self_attn.o_proj
	- model.layers.60.self_attn.o_proj
	- model.layers.23.self_attn.o_proj
	- model.layers.18.self_attn.o_proj
	- model.layers.67.self_attn.o_proj
	- model.layers.57.self_attn.o_proj
	- model.layers.20.self_attn.o_proj
	- model.layers.76.self_attn.o_proj
	- model.layers.28.self_attn.o_proj
	# self_attn.q_proj layers
	- model.layers.1.self_attn.q_proj
	- model.layers.6.self_attn.q_proj
	- model.layers.0.self_attn.q_proj
	- model.layers.5.self_attn.q_proj
	- model.layers.2.self_attn.q_proj
	- model.layers.7.self_attn.q_proj
	- model.layers.3.self_attn.q_proj
	- model.layers.4.self_attn.q_proj
	- model.layers.8.self_attn.q_proj
	- model.layers.9.self_attn.q_proj
	- model.layers.61.self_attn.q_proj
	- model.layers.10.self_attn.q_proj
	- model.layers.62.self_attn.q_proj
	- model.layers.36.self_attn.q_proj
	- model.layers.15.self_attn.q_proj
	- model.layers.11.self_attn.q_proj
	- model.layers.17.self_attn.q_proj
	- model.layers.60.self_attn.q_proj
	- model.layers.63.self_attn.q_proj
	- model.layers.64.self_attn.q_proj
	- model.layers.29.self_attn.q_proj
	- model.layers.30.self_attn.q_proj
	- model.layers.55.self_attn.q_proj
	- model.layers.34.self_attn.q_proj
	# self_attn.v_proj layers
	- model.layers.12.self_attn.v_proj
	- model.layers.16.self_attn.v_proj
	- model.layers.18.self_attn.v_proj
	- model.layers.19.self_attn.v_proj
	- model.layers.20.self_attn.v_proj
	- model.layers.21.self_attn.v_proj
	- model.layers.22.self_attn.v_proj
	- model.layers.23.self_attn.v_proj
	- model.layers.24.self_attn.v_proj
	- model.layers.25.self_attn.v_proj
	- model.layers.26.self_attn.v_proj
	- model.layers.27.self_attn.v_proj
	- model.layers.28.self_attn.v_proj
	- model.layers.29.self_attn.v_proj
	- model.layers.30.self_attn.v_proj
	- model.layers.31.self_attn.v_proj
	- model.layers.32.self_attn.v_proj
	- model.layers.33.self_attn.v_proj
	- model.layers.34.self_attn.v_proj
	- model.layers.35.self_attn.v_proj
	- model.layers.36.self_attn.v_proj
	- model.layers.37.self_attn.v_proj
	- model.layers.38.self_attn.v_proj
	- model.layers.39.self_attn.v_proj



	sequence_len: 8192 # supports up to 8192
	sample_packing: true
	pad_to_sequence_len: true

	# adapter: lora
	# lora_model_dir:
	# lora_r: 32
	# lora_alpha: 16
	# lora_dropout: 0.05
	# lora_target_linear: true
	# lora_fan_in_fan_out:

	wandb_project: dolphin-2.9-qwen-1.5-110b
	wandb_entity:
	wandb_watch:
	wandb_name:
	wandb_log_model:

	gradient_accumulation_steps: 8
	micro_batch_size: 1
	num_epochs: 1
	optimizer: adamw_8bit
	lr_scheduler: cosine
	learning_rate: 1e-5

	train_on_inputs: false
	group_by_length: false
	bf16: auto
	fp16:
	tf32: true

	gradient_checkpointing: true
	early_stopping_patience:
	# resume_from_checkpoint: /workspace/axolotl/qwen-checkpoint
	local_rank:
	logging_steps: 1
	xformers_attention:
	flash_attention: true

	warmup_steps: 10
	evals_per_epoch: 4
	eval_table_size:
	eval_max_new_tokens: 128
	saves_per_epoch: 4
	save_total_limit: 2
	debug:
	deepspeed: deepspeed_configs/zero3_bf16_cpuoffload_params.json
	weight_decay: 0.0
	fsdp:
	fsdp_config:
	special_tokens:
	eos_token: "<\|im_end\|>"



	```

	</details><br>

	## Training procedure

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 1e-05
	- train_batch_size: 1
	- eval_batch_size: 1
	- seed: 42
	- distributed_type: multi-GPU
	- num_devices: 8
	- gradient_accumulation_steps: 8
	- total_train_batch_size: 64
	- total_eval_batch_size: 8
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: cosine
	- lr_scheduler_warmup_steps: 10
	- num_epochs: 1

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|
	\| 0.3528 \| 0.0 \| 1 \| 0.3848 \|
	\| 0.3687 \| 0.25 \| 291 \| 0.3988 \|
	\| 0.4156 \| 0.5 \| 582 \| 0.3966 \|
	\| 0.3826 \| 0.75 \| 873 \| 0.3931 \|


	### Framework versions

	- Transformers 4.40.0.dev0
	- Pytorch 2.2.2+cu121
	- Datasets 2.15.0
	- Tokenizers 0.15.0