π‘ pandafish
Collection
based on mistral 7b β’ 4 items β’ Updated β’ 1
How to use ichigoberry/pandafish-2-7b-32k with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="ichigoberry/pandafish-2-7b-32k") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("ichigoberry/pandafish-2-7b-32k")
model = AutoModelForCausalLM.from_pretrained("ichigoberry/pandafish-2-7b-32k")How to use ichigoberry/pandafish-2-7b-32k with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ichigoberry/pandafish-2-7b-32k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "ichigoberry/pandafish-2-7b-32k",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/ichigoberry/pandafish-2-7b-32k
How to use ichigoberry/pandafish-2-7b-32k with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "ichigoberry/pandafish-2-7b-32k" \
--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": "ichigoberry/pandafish-2-7b-32k",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "ichigoberry/pandafish-2-7b-32k" \
--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": "ichigoberry/pandafish-2-7b-32k",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use ichigoberry/pandafish-2-7b-32k with Docker Model Runner:
docker model run hf.co/ichigoberry/pandafish-2-7b-32k
pandafish-2-7b-32k is a merge of the following models using LazyMergekit:
Playground on Huggingface Space
Chat template: Mistral Instruct
| Model | AGIEval | GPT4All | TruthfulQA | Bigbench | Average |
|---|---|---|---|---|---|
| π‘ pandafish-2-7b-32k π | 40.8 | 73.35 | 57.46 | 42.69 | 53.57 |
| Mistral-7B-Instruct-v0.2 π | 38.5 | 71.64 | 66.82 | 42.29 | 54.81 |
| dolphin-2.8-mistral-7b-v02 π | 38.99 | 72.22 | 51.96 | 40.41 | 50.9 |
models:
- model: alpindale/Mistral-7B-v0.2-hf
# No parameters necessary for base model
- model: mistralai/Mistral-7B-Instruct-v0.2
parameters:
density: 0.53
weight: 0.4
- model: cognitivecomputations/dolphin-2.8-mistral-7b-v02
parameters:
density: 0.53
weight: 0.4
merge_method: dare_ties
base_model: alpindale/Mistral-7B-v0.2-hf
parameters:
int8_mask: true
dtype: bfloat16
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "ichigoberry/pandafish-2-7b-32k"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])