Update README.md
Browse files
README.md
CHANGED
@@ -14,7 +14,7 @@ tags:
|
|
14 |
# OpenReasoning-Nemotron-1.5B Overview
|
15 |
|
16 |
## Description: <br>
|
17 |
-
OpenReasoning-Nemotron-1.5B is a large language model (LLM) which is a derivative of Qwen2.5-1.5B-Instruct (AKA the reference model). It is a reasoning model that is post-trained for reasoning about math, code and science solution generation. The model supports a context length of
|
18 |
|
19 |
This model is ready for commercial/non-commercial research use. <br>
|
20 |
|
@@ -88,7 +88,7 @@ messages = [
|
|
88 |
]
|
89 |
outputs = pipeline(
|
90 |
messages,
|
91 |
-
max_new_tokens=
|
92 |
)
|
93 |
print(outputs[0]["generated_text"][-1]['content'])
|
94 |
````
|
@@ -167,13 +167,13 @@ Network Architecture: Qwen-1.5B-Instruct
|
|
167 |
**Input Type(s):** Text <br>
|
168 |
**Input Format(s):** String <br>
|
169 |
**Input Parameters:** One-Dimensional (1D) <br>
|
170 |
-
**Other Properties Related to Input:** Context length up to
|
171 |
|
172 |
## Output: <br>
|
173 |
**Output Type(s):** Text <br>
|
174 |
**Output Format:** String <br>
|
175 |
**Output Parameters:** One-Dimensional (1D) <br>
|
176 |
-
**Other Properties Related to Output:** Context length up to
|
177 |
|
178 |
Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions. <br>
|
179 |
|
|
|
14 |
# OpenReasoning-Nemotron-1.5B Overview
|
15 |
|
16 |
## Description: <br>
|
17 |
+
OpenReasoning-Nemotron-1.5B is a large language model (LLM) which is a derivative of Qwen2.5-1.5B-Instruct (AKA the reference model). It is a reasoning model that is post-trained for reasoning about math, code and science solution generation. The model supports a context length of 64K tokens. The OpenReasoning model is available in the following sizes: 1.5B, 7B and 14B and 32B. <br>
|
18 |
|
19 |
This model is ready for commercial/non-commercial research use. <br>
|
20 |
|
|
|
88 |
]
|
89 |
outputs = pipeline(
|
90 |
messages,
|
91 |
+
max_new_tokens=64000,
|
92 |
)
|
93 |
print(outputs[0]["generated_text"][-1]['content'])
|
94 |
````
|
|
|
167 |
**Input Type(s):** Text <br>
|
168 |
**Input Format(s):** String <br>
|
169 |
**Input Parameters:** One-Dimensional (1D) <br>
|
170 |
+
**Other Properties Related to Input:** Context length up to 64,000 tokens <br>
|
171 |
|
172 |
## Output: <br>
|
173 |
**Output Type(s):** Text <br>
|
174 |
**Output Format:** String <br>
|
175 |
**Output Parameters:** One-Dimensional (1D) <br>
|
176 |
+
**Other Properties Related to Output:** Context length up to 64,000 tokens <br>
|
177 |
|
178 |
Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions. <br>
|
179 |
|