Hugging Face's logo

jayakody2000lk
/
granite-3.0-8b-instruct-Q5_K_M-GGUF

Text Generation
Transformers
GGUF
language
granite-3.0
llama-cpp
gguf-my-repo
Eval Results
conversational
Model card Files Community
granite-3.0-8b-instruct-Q5_K_M-GGUF
File size: 4,223 Bytes 
5c63abe
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
 
---
pipeline_tag: text-generation
inference: false
license: apache-2.0
library_name: transformers
tags:
- language
- granite-3.0
- llama-cpp
- gguf-my-repo
base_model: ibm-granite/granite-3.0-8b-instruct
model-index:
- name: granite-3.0-2b-instruct
  results:
  - task:
      type: text-generation
    dataset:
      name: IFEval
      type: instruction-following
    metrics:
    - type: pass@1
      value: 52.27
      name: pass@1
    - type: pass@1
      value: 8.22
      name: pass@1
  - task:
      type: text-generation
    dataset:
      name: AGI-Eval
      type: human-exams
    metrics:
    - type: pass@1
      value: 40.52
      name: pass@1
    - type: pass@1
      value: 65.82
      name: pass@1
    - type: pass@1
      value: 34.45
      name: pass@1
  - task:
      type: text-generation
    dataset:
      name: OBQA
      type: commonsense
    metrics:
    - type: pass@1
      value: 46.6
      name: pass@1
    - type: pass@1
      value: 71.21
      name: pass@1
    - type: pass@1
      value: 82.61
      name: pass@1
    - type: pass@1
      value: 77.51
      name: pass@1
    - type: pass@1
      value: 60.32
      name: pass@1
  - task:
      type: text-generation
    dataset:
      name: BoolQ
      type: reading-comprehension
    metrics:
    - type: pass@1
      value: 88.65
      name: pass@1
    - type: pass@1
      value: 21.58
      name: pass@1
  - task:
      type: text-generation
    dataset:
      name: ARC-C
      type: reasoning
    metrics:
    - type: pass@1
      value: 64.16
      name: pass@1
    - type: pass@1
      value: 33.81
      name: pass@1
    - type: pass@1
      value: 51.55
      name: pass@1
  - task:
      type: text-generation
    dataset:
      name: HumanEvalSynthesis
      type: code
    metrics:
    - type: pass@1
      value: 64.63
      name: pass@1
    - type: pass@1
      value: 57.16
      name: pass@1
    - type: pass@1
      value: 65.85
      name: pass@1
    - type: pass@1
      value: 49.6
      name: pass@1
  - task:
      type: text-generation
    dataset:
      name: GSM8K
      type: math
    metrics:
    - type: pass@1
      value: 68.99
      name: pass@1
    - type: pass@1
      value: 30.94
      name: pass@1
  - task:
      type: text-generation
    dataset:
      name: PAWS-X (7 langs)
      type: multilingual
    metrics:
    - type: pass@1
      value: 64.94
      name: pass@1
    - type: pass@1
      value: 48.2
      name: pass@1
---

# jayakody2000lk/granite-3.0-8b-instruct-Q5_K_M-GGUF
This model was converted to GGUF format from [`ibm-granite/granite-3.0-8b-instruct`](https://huggingface.co/ibm-granite/granite-3.0-8b-instruct) 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/ibm-granite/granite-3.0-8b-instruct) for more details on the model.

## 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 jayakody2000lk/granite-3.0-8b-instruct-Q5_K_M-GGUF --hf-file granite-3.0-8b-instruct-q5_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo jayakody2000lk/granite-3.0-8b-instruct-Q5_K_M-GGUF --hf-file granite-3.0-8b-instruct-q5_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 jayakody2000lk/granite-3.0-8b-instruct-Q5_K_M-GGUF --hf-file granite-3.0-8b-instruct-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo jayakody2000lk/granite-3.0-8b-instruct-Q5_K_M-GGUF --hf-file granite-3.0-8b-instruct-q5_k_m.gguf -c 2048
```