View source for 2024 llama.cpp 7840u


I briefly had a Macbook M3 Max with 64GB. It was pretty good at running local LLMs, but couldn't stand the ergonomics and not being able to run Linux, so returned it.

I picked up a Thinkpad P16s with an AMD 7840u to give Linux hardware a chance to catch up with Apple silicon. It's an amazing computer for the price, and can run LLMs. Here's how I set up llama.cpp to use ROCm.

Install ROCm, set an env variable for the 780m: <code>export HSA_OVERRIDE_GFX_VERSION=11.0.0</code>

clone llama.cpp and compile it:

<code>make -j16 LLAMA_HIPBLAS=1 LLAMA_HIP_UMA=1 AMDGPU_TARGETS=gfx1030 DLLAMA_HIP_UMA=
ON</code>
 
run it like this:
 
<code>./main -m /home/vid/jan/models/mistral-ins-7b-q4/mistral-7b-instruct-v0.2.Q4_K_M
.gguf -p "example code for a lit Web Component that reverses a string" -n 50 -e 
-ngl 33  -n -1</code>

 ggml_cuda_init: found 1 ROCm devices:
   Device 0: AMD Radeon Graphics, compute capability 11.0, VMM: no
 llm_load_tensors: ggml ctx size =    0.22 MiB
 llm_load_tensors: offloading 32 repeating layers to GPU
 llm_load_tensors: offloading non-repeating layers to GPU
 llm_load_tensors: offloaded 33/33 layers to GPU
 llm_load_tensors:      ROCm0 buffer size =  4095.05 MiB
 llm_load_tensors:        CPU buffer size =    70.31 MiB
 ..............................................................................................
 llama_new_context_with_model: n_ctx      = 512
 llama_new_context_with_model: n_batch    = 512
 llama_new_context_with_model: n_ubatch   = 512
 llama_new_context_with_model: freq_base  = 1000000.0
 llama_new_context_with_model: freq_scale = 1
 llama_kv_cache_init:      ROCm0 KV buffer size =    64.00 MiB
 llama_new_context_with_model: KV self size  =   64.00 MiB, K (f16):   32.00 MiB, V (f16):   32.00 MiB
 llama_new_context_with_model:  ROCm_Host  output buffer size =     0.12 MiB
 llama_new_context_with_model:      ROCm0 compute buffer size =    81.00 MiB
 llama_new_context_with_model:  ROCm_Host compute buffer size =     9.01 MiB
 llama_new_context_with_model: graph nodes  = 1030
 llama_new_context_with_model: graph splits = 2
 
 system_info: n_threads = 8 / 16 | AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 |
 sampling:
         repeat_last_n = 64, repeat_penalty = 1.000, frequency_penalty = 0.000, presence_penalty = 0.000
         top_k = 40, tfs_z = 1.000, top_p = 0.950, min_p = 0.050, typical_p = 1.000, temp = 0.800
         mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000
 sampling order:
 CFG -> Penalties -> top_k -> tfs_z -> typical_p -> top_p -> min_p -> temperature
 generate: n_ctx = 512, n_batch = 2048, n_predict = -1, n_keep = 1
 
 
  simple example code for a lit Web Component that reverses a string
 
 ```javascript
 import { LitElement, html } from 'lit';
 import { customElement, property } from 'lit/decorators.js';
 
 @customElement('reverse-string')
 class ReverseString extends LitElement {
 
   static styles = css`
     :host {
       display: block;
     }
   `;
 
   @property({ type: String }) input = '';
 
   render() {
     return html`
       <input type="text" value=${this.input} @input=${this._handleInput} />
       <button @click=${this._reverse}>Reverse</button>
       < p>${this._reversed}< /p>
     `;
   }
 
   _handleInput(event) {
     this.input = event.target.value;
   }
 
   _reverse() {
     this._reversed = this.input.split('').reverse().join('');
   }
 
   @property private _reversed = '';
 }
 ```
 
 ```css
 :host {
   display: block;
 }
 ```
 
 This is a simple example of a Lit Web Component that reverses a string. The component has an input field for the user to enter a string, and a button to reverse the string when clicked. The reversed string is displayed below the button.
 
 The component uses the Lit library to define the custom element, and uses the `@customElement` decorator to define the element's name as 'reverse-string'. The `@property` decorator is used to define the input property, and the `static styles` property is used to define the component's styles.
 
 In the `render` method, the input field and button are created using template literals, and the reversed string is displayed using a reactive property `_reversed`.
 
 The input field's value is updated in the `_handleInput` method when the user types in the field, and the string is reversed in the `_reverse` method when the button is clicked. The reversed string is then assigned to the `_reversed` property, which updates the displayed string. [end of text]
  
 llama_print_timings:        load time =    2488.76 ms
 llama_print_timings:      sample time =      24.92 ms /   485 runs   (    0.05 ms per token, 19462.28 tokens per second)
 llama_print_timings: prompt eval time =     576.45 ms /    14 tokens (   41.18 ms per token,    24.29 tokens per second)
 llama_print_timings:        eval time =   38985.11 ms /   484 runs   (   80.55 ms per token,    12.41 tokens per second)
 llama_print_timings:       total time =   39890.17 ms /   498 tokens
 Log end


It's definitely not going to win any speed prizes even though is a smaller model, but it could be ok for non time sensitive results, or where using a tiny, faster model is useful.

{{Blikied|April 13, 2024}}