Update README.md
Browse files
README.md
CHANGED
@@ -2,4 +2,102 @@
|
|
2 |
base_model:
|
3 |
- Qwen/Qwen2.5-3B-Instruct
|
4 |
library_name: transformers
|
5 |
-
---
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
2 |
base_model:
|
3 |
- Qwen/Qwen2.5-3B-Instruct
|
4 |
library_name: transformers
|
5 |
+
---
|
6 |
+

|
7 |
+
|
8 |
+
# **Maverick-1-3B Model Card**
|
9 |
+
|
10 |
+
## **Model Overview**
|
11 |
+
|
12 |
+
**Maverick-1-3B** is a 3.09-billion-parameter causal language model fine-tuned from Qwen2.5-3B-Instruct. This model is designed to excel in various natural language processing tasks, offering enhanced reasoning and instruction-following capabilities.
|
13 |
+
|
14 |
+
## **Model Details**
|
15 |
+
|
16 |
+
- **Model Developer:** Aayan Mishra
|
17 |
+
- **Model Type:** Causal Language Model
|
18 |
+
- **Architecture:** Transformer with Rotary Position Embeddings (RoPE), SwiGLU activation, RMSNorm, Attention QKV bias, and tied word embeddings
|
19 |
+
- **Parameters:** 3.09 billion total (2.77 billion non-embedding)
|
20 |
+
- **Layers:** 36
|
21 |
+
- **Attention Heads:** 16 for query and 2 for key-value (Grouped Query Attention)
|
22 |
+
- **Vocabulary Size:** Approximately 151,646 tokens
|
23 |
+
- **Context Length:** Supports up to 32,768 tokens
|
24 |
+
- **Languages Supported:** Primarily English, with basic support for other languages
|
25 |
+
- **License:** MIT
|
26 |
+
|
27 |
+
## **Training Details**
|
28 |
+
|
29 |
+
Maverick-1-3B was fine-tuned using the Unsloth framework on a single NVIDIA A100 GPU. The fine-tuning process spanned approximately 90 minutes over 60 epochs, utilizing a curated dataset focused on instruction-following and general NLP tasks. This approach aimed to enhance the model's performance in complex reasoning and academic tasks.
|
30 |
+
|
31 |
+
## **Intended Use**
|
32 |
+
|
33 |
+
Maverick-1-3B is designed for a range of applications, including but not limited to:
|
34 |
+
|
35 |
+
- **General NLP Tasks:** Engaging in text completion, summarization, and question-answering tasks.
|
36 |
+
- **Academic Assistance:** Providing support for tutoring, essay composition, and research inquiries.
|
37 |
+
- **Data Analysis:** Offering insights and interpretations of data-centric queries.
|
38 |
+
|
39 |
+
While Maverick-1-3B is a powerful tool for various applications, it is not intended for real-time, safety-critical systems or for processing sensitive personal information.
|
40 |
+
|
41 |
+
## **How to Use**
|
42 |
+
|
43 |
+
To utilize Maverick-1-3B, ensure that you have the latest version of the `transformers` library installed:
|
44 |
+
|
45 |
+
```bash
|
46 |
+
pip install transformers
|
47 |
+
```
|
48 |
+
|
49 |
+
Here's an example of how to load the Maverick-1-3B model and generate a response:
|
50 |
+
|
51 |
+
```python
|
52 |
+
from transformers import AutoModelForCausalLM, AutoTokenizer
|
53 |
+
|
54 |
+
model_name = "Spestly/Maverick-1-3B"
|
55 |
+
model = AutoModelForCausalLM.from_pretrained(
|
56 |
+
model_name,
|
57 |
+
torch_dtype="auto",
|
58 |
+
device_map="auto"
|
59 |
+
)
|
60 |
+
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
61 |
+
|
62 |
+
prompt = "Explain the concept of entropy in thermodynamics."
|
63 |
+
messages = [
|
64 |
+
{"role": "system", "content": "You are Maverick, an AI assistant designed to be helpful."},
|
65 |
+
{"role": "user", "content": prompt}
|
66 |
+
]
|
67 |
+
text = tokenizer.apply_chat_template(
|
68 |
+
messages,
|
69 |
+
tokenize=False,
|
70 |
+
add_generation_prompt=True
|
71 |
+
)
|
72 |
+
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
|
73 |
+
generated_ids = model.generate(
|
74 |
+
**model_inputs,
|
75 |
+
max_new_tokens=512
|
76 |
+
)
|
77 |
+
generated_ids = [
|
78 |
+
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
|
79 |
+
]
|
80 |
+
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
|
81 |
+
print(response)
|
82 |
+
```
|
83 |
+
|
84 |
+
## **Limitations**
|
85 |
+
|
86 |
+
Users should be aware of the following limitations:
|
87 |
+
|
88 |
+
- **Biases:** Maverick-1-3B may exhibit biases present in its training data. Users should critically assess outputs, especially in sensitive contexts.
|
89 |
+
- **Knowledge Cutoff:** The model's knowledge is current up to August 2024. It may not be aware of events or developments occurring after this date.
|
90 |
+
- **Language Support:** While primarily trained on English data, performance in other languages may be inconsistent.
|
91 |
+
|
92 |
+
## **Acknowledgements**
|
93 |
+
|
94 |
+
Maverick-1-3B builds upon the work of the Qwen team. Gratitude is also extended to the open-source AI community for their contributions to tools and frameworks that facilitated the development of Maverick-1-3B.
|
95 |
+
|
96 |
+
## **License**
|
97 |
+
|
98 |
+
Maverick-1-3B is released under the MIT License, permitting wide usage with proper attribution.
|
99 |
+
|
100 |
+
## **Contact**
|
101 |
+
|
102 |
+
- Email: maverick@aayanmishra.com
|
103 |
+
|