Hugging Face's logo

Triangle104
/
AwA-1.5B-Q4_K_M-GGUF

Transformers
GGUF
English
text-generation-inference
unsloth
qwen2
trl
llama-cpp
gguf-my-repo
Inference Endpoints
conversational
Model card Files Community
AwA-1.5B-Q4_K_M-GGUF
File size: 4,180 Bytes 
2bc8c71
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2359a56
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2bc8c71
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
 
---
base_model: Spestly/AwA-1.5B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- llama-cpp
- gguf-my-repo
license: apache-2.0
language:
- en
library_name: transformers
---

# Triangle104/AwA-1.5B-Q4_K_M-GGUF
This model was converted to GGUF format from [`Spestly/AwA-1.5B`](https://huggingface.co/Spestly/AwA-1.5B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Spestly/AwA-1.5B) for more details on the model.

---
Model details:
-
AwA (Answers with Athena) is my portfolio project, showcasing a cutting-edge Chain-of-Thought (CoT) reasoning model. I created AwA to excel in providing detailed, step-by-step answers to complex questions across diverse domains. This model represents my dedication to advancing AI’s capability for enhanced comprehension, problem-solving, and knowledge synthesis.
Key Features

    Chain-of-Thought Reasoning: AwA delivers step-by-step breakdowns of solutions, mimicking logical human thought processes.

    Domain Versatility: Performs exceptionally across a wide range of domains, including mathematics, science, literature, and more.

    Adaptive Responses: Adjusts answer depth and complexity based on input queries, catering to both novices and experts.

    Interactive Design: Designed for educational tools, research assistants, and decision-making systems.

Intended Use Cases

    Educational Applications: Supports learning by breaking down complex problems into manageable steps.

    Research Assistance: Generates structured insights and explanations in academic or professional research.

    Decision Support: Enhances understanding in business, engineering, and scientific contexts.

    General Inquiry: Provides coherent, in-depth answers to everyday questions.

Type: Chain-of-Thought (CoT) Reasoning Model

    Base Architecture: Adapted from [qwen2]

    Parameters: [1.54B]

    Fine-tuning: Specialized fine-tuning on Chain-of-Thought reasoning datasets to enhance step-by-step explanatory capabilities.

Ethical Considerations

    Bias Mitigation: I have taken steps to minimise biases in the training data. However, users are encouraged to cross-verify outputs in sensitive contexts.

    Limitations: May not provide exhaustive answers for niche topics or domains outside its training scope.

    User Responsibility: Designed as an assistive tool, not a replacement for expert human judgment.

Usage
Option A: Local

Using locally with the Transformers library

# Use a pipeline as a high-level helper
from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Spestly/AwA-1.5B")
pipe(messages)

Option B: API & Space

You can use the AwA HuggingFace space or the AwA API (Coming soon!)
Roadmap

    More AwA model sizes e.g 7B and 14B
    Create AwA API via spestly package

---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo Triangle104/AwA-1.5B-Q4_K_M-GGUF --hf-file awa-1.5b-q4_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/AwA-1.5B-Q4_K_M-GGUF --hf-file awa-1.5b-q4_k_m.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```

Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```

Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/AwA-1.5B-Q4_K_M-GGUF --hf-file awa-1.5b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/AwA-1.5B-Q4_K_M-GGUF --hf-file awa-1.5b-q4_k_m.gguf -c 2048
```