Upload README.md with huggingface_hub
Browse files
README.md
CHANGED
|
@@ -184,13 +184,14 @@ Reported numbers use the methodology described above.
|
|
| 184 |
### Quantitative Results (Inference Performance)
|
| 185 |
|
| 186 |
#### Metrics reported
|
| 187 |
-
- **System Output Throughput**: Mean output tokens per second across all concurrent requests over the benchmarking phase.
|
| 188 |
-
- **End-to-End Latency per Query:** Median end-to-end response time for each query from the time the query is sent.
|
| 189 |
-
- **Output Speed per Query:** Median output tokens per second after the first token is received for each query.
|
| 190 |
-
- **Time to first token (TTFT):** Median
|
| 191 |
-
- **Estimated Peak Memory Usage:** KV cache utilization is monitored during the phase and we estimate memory usage as follows: $model\_ weights_{gb} + kv\_ cache_{usage\_pct} × (nvml\_used_{gb} − model\_ weights_{gb})$
|
| 192 |
-
- **Model weights:**
|
| 193 |
-
|
|
|
|
| 194 |
|
| 195 |
#### Performance evaluation conditions
|
| 196 |
|
|
@@ -205,7 +206,7 @@ Our performance evaluation follows the spirit of [Artificial Analysis](https://a
|
|
| 205 |
- **Streaming**: Benchmarking is conducted with streaming enabled.
|
| 206 |
|
| 207 |
|
| 208 |
-
**Summary of improvements:**
|
| 209 |
|
| 210 |

|
| 211 |
|
|
|
|
| 184 |
### Quantitative Results (Inference Performance)
|
| 185 |
|
| 186 |
#### Metrics reported
|
| 187 |
+
- **System Output Throughput (higher is better)**: Mean output tokens per second across all concurrent requests over the benchmarking phase.
|
| 188 |
+
- **End-to-End Latency per Query (lower is better):** Median end-to-end response time for each query from the time the query is sent.
|
| 189 |
+
- **Output Speed per Query (higher is better):** Median output tokens per second after the first token is received for each query.
|
| 190 |
+
- **Time to first token (TTFT) (lower is better):** Median
|
| 191 |
+
- **Estimated Peak Memory Usage (lower is better):** KV cache utilization is monitored during the phase and we estimate memory usage as follows: $model\_ weights_{gb} + kv\_ cache_{usage\_pct} × (nvml\_used_{gb} − model\_ weights_{gb})$
|
| 192 |
+
- **Model weights (lower is better):**
|
| 193 |
+
|
| 194 |
+
**Summary of improvements:** LittleLamb shows a slight improvement in performance with respect to the original Qwen Model. This is expected as for such small models, VRAM usage is dominated by KV cache and not model weights.
|
| 195 |
|
| 196 |
#### Performance evaluation conditions
|
| 197 |
|
|
|
|
| 206 |
- **Streaming**: Benchmarking is conducted with streaming enabled.
|
| 207 |
|
| 208 |
|
| 209 |
+
**Summary of improvements:** LittleLamb shows a slight improvement in performance with respect to the original Qwen Model. This is expected as for such small models, VRAM usage is dominated by KV cache and not model weights.
|
| 210 |
|
| 211 |

|
| 212 |
|