Hugging Face's logo

prithivMLmods
/
Deepthink-Reasoning-Adapter

Text Generation
PEFT
Safetensors
English
text-generation-inference
code-solve
algorithm
codepy
qwen_base
7b
LoRA
CoT
conversational
Model card Files
xet
Community
1

Improve language tag

#1
by lbourdois - opened
base: refs/heads/main
←
from: refs/pr/1
Discussion Files changed
+118
-106
Files changed (1) hide show
  1. README.md +118 -106
README.md CHANGED
@@ -1,107 +1,119 @@
1
- ---
2
- license: creativeml-openrail-m
3
- language:
4
- - en
5
- base_model:
6
- - Qwen/Qwen2.5-7B-Instruct
7
- pipeline_tag: text-generation
8
- tags:
9
- - text-generation-inference
10
- - code-solve
11
- - algorithm
12
- - codepy
13
- - qwen_base
14
- - 7b
15
- - LoRA
16
- - CoT
17
- library_name: peft
18
- ---
19
-
20
- <pre align="center">
21
- .___ __ .__ .__ __
22
- __| _/ ____ ____ ______ _/ |_ | |__ |__| ____ | | __
23
- / __ | _/ __ \ _/ __ \ \____ \ \ __\| | \ | | / \ | |/ /
24
- / /_/ | \ ___/ \ ___/ | |_> > | | | Y \| || | \| <
25
- \____ | \___ > \___ >| __/ |__| |___| /|__||___| /|__|_ \
26
- \/ \/ \/ |__| \/ \/ \/
27
- </pre>
28
-
29
- The **Deepthink-Reasoning-Adapter** is a fine-tuned version of the **Qwen2.5-7B-Instruct** base model, designed for text generation tasks that require deep reasoning, logical structuring, and problem-solving. This model leverages its optimized architecture to provide accurate and contextually relevant outputs for complex queries, making it ideal for applications in education, programming, and creative writing.
30
-
31
- With its robust natural language processing capabilities, **Deepthink-Reasoning-Adapter** excels in generating step-by-step solutions, creative content, and logical analyses. Its architecture integrates advanced understanding of both structured and unstructured data, ensuring precise text generation aligned with user inputs.
32
-
33
- - Significantly **more knowledge** and has greatly improved capabilities in **coding** and **mathematics**, thanks to our specialized expert models in these domains.
34
- - Significant improvements in **instruction following**, **generating long texts** (over 8K tokens), **understanding structured data** (e.g, tables), and **generating structured outputs** especially JSON. **More resilient to the diversity of system prompts**, enhancing role-play implementation and condition-setting for chatbots.
35
- - **Long-context Support** up to 128K tokens and can generate up to 8K tokens.
36
- - **Multilingual support** for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
37
-
38
- # **Demo Start**
39
-
40
- Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
41
-
42
- ```python
43
- from transformers import AutoModelForCausalLM, AutoTokenizer
44
-
45
- model_name = "prithivMLmods/Deepthink-Reasoning-7B"
46
-
47
- model = AutoModelForCausalLM.from_pretrained(
48
- model_name,
49
- torch_dtype="auto",
50
- device_map="auto"
51
- )
52
- tokenizer = AutoTokenizer.from_pretrained(model_name)
53
-
54
- prompt = "Give me a short introduction to large language model."
55
- messages = [
56
- {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
57
- {"role": "user", "content": prompt}
58
- ]
59
- text = tokenizer.apply_chat_template(
60
- messages,
61
- tokenize=False,
62
- add_generation_prompt=True
63
- )
64
- model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
65
-
66
- generated_ids = model.generate(
67
- **model_inputs,
68
- max_new_tokens=512
69
- )
70
- generated_ids = [
71
- output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
72
- ]
73
-
74
- response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
75
- ```
76
- # **Run with Ollama [Ollama Run]**
77
-
78
- Ollama makes running machine learning models simple and efficient. Follow these steps to set up and run your GGUF models quickly.
79
-
80
- ## Quick Start: Step-by-Step Guide
81
-
82
- | Step | Description | Command / Instructions |
83
- |------|-------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------|
84
- | 1 | **Install Ollama 🦙** | Download Ollama from [https://ollama.com/download](https://ollama.com/download) and install it on your system. |
85
- | 2 | **Create Your Model File** | - Create a file named after your model, e.g., `metallama`. |
86
- | | | - Add the following line to specify the base model: |
87
- | | | ```bash |
88
- | | | FROM Llama-3.2-1B.F16.gguf |
89
- | | | ``` |
90
- | | | - Ensure the base model file is in the same directory. |
91
- | 3 | **Create and Patch the Model** | Run the following commands to create and verify your model: |
92
- | | | ```bash |
93
- | | | ollama create metallama -f ./metallama |
94
- | | | ollama list |
95
- | | | ``` |
96
- | 4 | **Run the Model** | Use the following command to start your model: |
97
- | | | ```bash |
98
- | | | ollama run metallama |
99
- | | | ``` |
100
- | 5 | **Interact with the Model** | Once the model is running, interact with it: |
101
- | | | ```plaintext |
102
- | | | >>> Tell me about Space X. |
103
- | | | Space X, the private aerospace company founded by Elon Musk, is revolutionizing space exploration... |
104
- | | | ``` |
105
-
106
- ## Conclusion
 
 
 
 
 
 
 
 
 
 
 
 
107
  With Ollama, running and interacting with models is seamless. Start experimenting today!
 
1
+ ---
2
+ license: creativeml-openrail-m
3
+ language:
4
+ - zho
5
+ - eng
6
+ - fra
7
+ - spa
8
+ - por
9
+ - deu
10
+ - ita
11
+ - rus
12
+ - jpn
13
+ - kor
14
+ - vie
15
+ - tha
16
+ - ara
17
+ base_model:
18
+ - Qwen/Qwen2.5-7B-Instruct
19
+ pipeline_tag: text-generation
20
+ tags:
21
+ - text-generation-inference
22
+ - code-solve
23
+ - algorithm
24
+ - codepy
25
+ - qwen_base
26
+ - 7b
27
+ - LoRA
28
+ - CoT
29
+ library_name: peft
30
+ ---
31
+
32
+ <pre align="center">
33
+ .___ __ .__ .__ __
34
+ __| _/ ____ ____ ______ _/ |_ | |__ |__| ____ | | __
35
+ / __ | _/ __ \ _/ __ \ \____ \ \ __\| | \ | | / \ | |/ /
36
+ / /_/ | \ ___/ \ ___/ | |_> > | | | Y \| || | \| <
37
+ \____ | \___ > \___ >| __/ |__| |___| /|__||___| /|__|_ \
38
+ \/ \/ \/ |__| \/ \/ \/
39
+ </pre>
40
+
41
+ The **Deepthink-Reasoning-Adapter** is a fine-tuned version of the **Qwen2.5-7B-Instruct** base model, designed for text generation tasks that require deep reasoning, logical structuring, and problem-solving. This model leverages its optimized architecture to provide accurate and contextually relevant outputs for complex queries, making it ideal for applications in education, programming, and creative writing.
42
+
43
+ With its robust natural language processing capabilities, **Deepthink-Reasoning-Adapter** excels in generating step-by-step solutions, creative content, and logical analyses. Its architecture integrates advanced understanding of both structured and unstructured data, ensuring precise text generation aligned with user inputs.
44
+
45
+ - Significantly **more knowledge** and has greatly improved capabilities in **coding** and **mathematics**, thanks to our specialized expert models in these domains.
46
+ - Significant improvements in **instruction following**, **generating long texts** (over 8K tokens), **understanding structured data** (e.g, tables), and **generating structured outputs** especially JSON. **More resilient to the diversity of system prompts**, enhancing role-play implementation and condition-setting for chatbots.
47
+ - **Long-context Support** up to 128K tokens and can generate up to 8K tokens.
48
+ - **Multilingual support** for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
49
+
50
+ # **Demo Start**
51
+
52
+ Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
53
+
54
+ ```python
55
+ from transformers import AutoModelForCausalLM, AutoTokenizer
56
+
57
+ model_name = "prithivMLmods/Deepthink-Reasoning-7B"
58
+
59
+ model = AutoModelForCausalLM.from_pretrained(
60
+ model_name,
61
+ torch_dtype="auto",
62
+ device_map="auto"
63
+ )
64
+ tokenizer = AutoTokenizer.from_pretrained(model_name)
65
+
66
+ prompt = "Give me a short introduction to large language model."
67
+ messages = [
68
+ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
69
+ {"role": "user", "content": prompt}
70
+ ]
71
+ text = tokenizer.apply_chat_template(
72
+ messages,
73
+ tokenize=False,
74
+ add_generation_prompt=True
75
+ )
76
+ model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
77
+
78
+ generated_ids = model.generate(
79
+ **model_inputs,
80
+ max_new_tokens=512
81
+ )
82
+ generated_ids = [
83
+ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
84
+ ]
85
+
86
+ response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
87
+ ```
88
+ # **Run with Ollama [Ollama Run]**
89
+
90
+ Ollama makes running machine learning models simple and efficient. Follow these steps to set up and run your GGUF models quickly.
91
+
92
+ ## Quick Start: Step-by-Step Guide
93
+
94
+ | Step | Description | Command / Instructions |
95
+ |------|-------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------|
96
+ | 1 | **Install Ollama 🦙** | Download Ollama from [https://ollama.com/download](https://ollama.com/download) and install it on your system. |
97
+ | 2 | **Create Your Model File** | - Create a file named after your model, e.g., `metallama`. |
98
+ | | | - Add the following line to specify the base model: |
99
+ | | | ```bash |
100
+ | | | FROM Llama-3.2-1B.F16.gguf |
101
+ | | | ``` |
102
+ | | | - Ensure the base model file is in the same directory. |
103
+ | 3 | **Create and Patch the Model** | Run the following commands to create and verify your model: |
104
+ | | | ```bash |
105
+ | | | ollama create metallama -f ./metallama |
106
+ | | | ollama list |
107
+ | | | ``` |
108
+ | 4 | **Run the Model** | Use the following command to start your model: |
109
+ | | | ```bash |
110
+ | | | ollama run metallama |
111
+ | | | ``` |
112
+ | 5 | **Interact with the Model** | Once the model is running, interact with it: |
113
+ | | | ```plaintext |
114
+ | | | >>> Tell me about Space X. |
115
+ | | | Space X, the private aerospace company founded by Elon Musk, is revolutionizing space exploration... |
116
+ | | | ``` |
117
+
118
+ ## Conclusion
119
  With Ollama, running and interacting with models is seamless. Start experimenting today!