Hugging Face's logo

blockblockblock
/
karasu-7B-chat-plus-bpw4-exl2

Text Generation
Transformers
Safetensors
Japanese
English
mistral
conversational
text-generation-inference
Inference Endpoints
4-bit precision
exl2
Model card Files Community
karasu-7B-chat-plus-bpw4-exl2
File size: 2,939 Bytes 
9bec3c5
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
 
---
license: apache-2.0
datasets:
- OpenAssistant/oasst1
- zetavg/ShareGPT-Processed
- augmxnt/ultra-orca-boros-en-ja-v1
language:
- ja
- en
---


<p align="center">
  <img src="https://cdn-uploads.huggingface.co/production/uploads/64c8a2e01c25d2c581a381c1/9CbN4lDGU42c-7DmK_mGM.png" alt="drawing" width="600"/>
</p>

# Evaluation


![image/png](https://cdn-uploads.huggingface.co/production/uploads/64b63f8ad57e02621dc93c8b/dYASMWRzqKjc-pZ8pDE7x.png)

# How to use


### Hugggingface
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("lightblue/karasu-7B-chat-plus")
model = AutoModelForCausalLM.from_pretrained("lightblue/karasu-7B-chat-plus", torch_dtype=torch.bfloat16, device_map="auto")

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

messages = [{"role": "system", "content": "あなたはAIアシスタントです。"}]
messages.append({"role": "user", "content": "イギリスの首相は誰ですか？"})

prompt = tokenizer.apply_chat_template(conversation=messages, add_generation_prompt=True, tokenize=False)

pipe(prompt, max_new_tokens=100, do_sample=False, temperature=0.0, return_full_text=False)
```


### VLLM
```python
from vllm import LLM, SamplingParams

sampling_params = SamplingParams(temperature=0.0, max_tokens=100)
llm = LLM(model="lightblue/karasu-7B-chat-plus")

messages = [{"role": "system", "content": "あなたはAIアシスタントです。"}]
messages.append({"role": "user", "content": "イギリスの首相は誰ですか？"})
prompt = llm.llm_engine.tokenizer.apply_chat_template(conversation=messages, add_generation_prompt=True, tokenize=False)
prompts = [prompt]

outputs = llm.generate(prompts, sampling_params)
for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```



# Base checkpoint
[lightblue/karasu-7B](https://huggingface.co./lightblue/karasu-7B)

# Training datasets (total ~7B)
* Lightblue's suite of Kujira datasets (unreleased)
* Lightblue's own question-based datasets (unreleased)
* Lightblue's own category-based datasets (unreleased)
* [OASST](https://huggingface.co./datasets/OpenAssistant/oasst1) (Japanese chats only)
* [ShareGPT](https://huggingface.co./datasets/zetavg/ShareGPT-Processed) (Japanese chats only)
* [augmxnt/ultra-orca-boros-en-ja-v1](https://huggingface.co./datasets/augmxnt/ultra-orca-boros-en-ja-v1) (['airoboros', 'slimorca', 'ultrafeedback', 'airoboros_ja_new'] only)

# Developed by

<a href="https://www.lightblue-tech.com">
<img src="https://www.lightblue-tech.com/wp-content/uploads/2023/08/color_%E6%A8%AA%E5%9E%8B-1536x469.png" alt="Lightblue technology logo" width="400"/>
</a>

### Engineers
Peter Devine

Sho Higuchi

### Advisors
Yuuki Yamanaka 

Atom Sonoda

### Project manager
Shunichi Taniguchi

### Dataset evaluator
Renju Aoki