Mistral 7b crashes permanently with GPU

# Prerequisites

Please answer the following questions for yourself before submitting an issue.

- [x ] I am running the latest code. Development is very rapid so there are no tagged versions as of now.
- [ x] I carefully followed the [README.md](https://github.com/abetlen/llama-cpp-python/blob/main/README.md).
- [ x] I [searched using keywords relevant to my issue](https://docs.github.com/en/issues/tracking-your-work-with-issues/filtering-and-searching-issues-and-pull-requests) to make sure that I am creating a new issue that is not already open (or closed).
- [ x] I reviewed the [Discussions](https://github.com/abetlen/llama-cpp-python/discussions), and have a new bug or useful enhancement to share.

# Expected Behavior

I run into a problem running llama-cpp-python with Mistral 7b with GPU/CUDA.

Onyl when I use small prompts like in the following example my `mistral-7b-instruct-v0.2.Q4_K_M.gguf` model works

```python
llm = Llama(model_path=model_path, n_gpu_layers=30, n_ctx=3584, n_batch=521, verbose=True)
output = llm("Q: Name and explain the planets in the solar system? A: ", max_tokens=2000, stop=["Q:", "\n"], echo=True)
print(output)
```

Outcome:

```
app-1 | llama_new_context_with_model: n_ctx = 3584
app-1 | llama_new_context_with_model: n_batch = 521
app-1 | llama_new_context_with_model: n_ubatch = 512
app-1 | llama_new_context_with_model: freq_base = 1000000.0
app-1 | llama_new_context_with_model: freq_scale = 1
app-1 | llama_kv_cache_init: CUDA_Host KV buffer size = 28.00 MiB
app-1 | llama_kv_cache_init: CUDA0 KV buffer size = 420.00 MiB
app-1 | llama_new_context_with_model: KV self size = 448.00 MiB, K (f16): 224.00 MiB, V (f16): 224.00 MiB
app-1 | llama_new_context_with_model: CUDA_Host output buffer size = 0.12 MiB
app-1 | llama_new_context_with_model: CUDA0 compute buffer size = 272.00 MiB
app-1 | llama_new_context_with_model: CUDA_Host compute buffer size = 15.01 MiB
app-1 | llama_new_context_with_model: graph nodes = 1030
app-1 | llama_new_context_with_model: graph splits = 26
app-1 | AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | 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 |
app-1 | --- Init Model...finished in 0.9686391353607178 sec
app-1 | --- compute prompt....
app-1 | start processing prompt:
app-1 |
app-1 | [INST] Q: Name and explain the planets in the solar system? A: [/INST]
app-1 | ...
app-1 |
app-1 | Model metadata: {'tokenizer.chat_template': "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}", 'tokenizer.ggml.add_eos_token': 'false', 'tokenizer.ggml.padding_token_id': '0', 'tokenizer.ggml.unknown_token_id': '0', 'tokenizer.ggml.eos_token_id': '2', 'general.architecture': 'llama', 'llama.rope.freq_base': '1000000.000000', 'llama.context_length': '32768', 'general.name': 'mistralai_mistral-7b-instruct-v0.2', 'tokenizer.ggml.add_bos_token': 'true', 'llama.embedding_length': '4096', 'llama.feed_forward_length': '14336', 'llama.attention.layer_norm_rms_epsilon': '0.000010', 'llama.rope.dimension_count': '128', 'tokenizer.ggml.bos_token_id': '1', 'llama.attention.head_count': '32', 'llama.block_count': '32', 'llama.attention.head_count_kv': '8', 'general.quantization_version': '2', 'tokenizer.ggml.model': 'llama', 'general.file_type': '15'}
app-1 | Guessed chat format: mistral-instruct
app-1 |
app-1 | llama_print_timings: load time = 253.05 ms
app-1 | llama_print_timings: sample time = 316.83 ms / 817 runs ( 0.39 ms per token, 2578.63 tokens per second)
app-1 | llama_print_timings: prompt eval time = 252.99 ms / 25 tokens ( 10.12 ms per token, 98.82 tokens per second)
app-1 | llama_print_timings: eval time = 36624.03 ms / 816 runs ( 44.88 ms per token, 22.28 tokens per second)
app-1 | llama_print_timings: total time = 39066.96 ms / 841 tokens
```

# Current Behavior

But if I try more complex prompts the model crashes with:

```
Llama.generate: prefix-match hit
app-1 exited with code 139
```

Than the only solution seems to reduce the param `n_gpu_layers` from a value of 30 to only 10. Also other parameters like `n_ctx` and `n_batch` can cause a crash.

This all only happens when I use the GPU. Without GPU the programm runs slow but without any chrashes.

# Environment and Context

Please provide detailed information about your computer setup. This is important in case the issue is not reproducible except for under certain specific conditions.

My hardware is a CPU Intel Core i7-7700 + GeForce GTX 1080. My programm runs in a Docker container based on nvidia/cuda:12.1.1-devel-ubuntu22.04

* Physical (or virtual) hardware you are using, e.g. for Linux:

`$ lscpu`

```
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 39 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 8
On-line CPU(s) list: 0-7
Vendor ID: GenuineIntel
Model name: Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz
CPU family: 6
Model: 158
Thread(s) per core: 2
Core(s) per socket: 4
Socket(s): 1
Stepping: 9
CPU(s) scaling MHz: 21%
CPU max MHz: 4200.0000
CPU min MHz: 800.0000
BogoMIPS: 7200.00
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht
tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc
cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 s
se4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb
invpcid_single pti ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 a
vx2 smep bmi2 erms invpcid mpx rdseed adx smap clflushopt intel_pt xsaveopt xsavec xgetbv1 xsaves dtherm ida ara
t pln pts hwp hwp_notify hwp_act_window hwp_epp md_clear flush_l1d arch_capabilities
Virtualization features:
Virtualization: VT-x
Caches (sum of all):
L1d: 128 KiB (4 instances)
L1i: 128 KiB (4 instances)
L2: 1 MiB (4 instances)
L3: 8 MiB (1 instance)
NUMA:
NUMA node(s): 1
NUMA node0 CPU(s): 0-7
Vulnerabilities:
Gather data sampling: Mitigation; Microcode
Itlb multihit: KVM: Mitigation: VMX disabled
L1tf: Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable
Mds: Mitigation; Clear CPU buffers; SMT vulnerable
Meltdown: Mitigation; PTI
Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable
Retbleed: Mitigation; IBRS
Spec rstack overflow: Not affected
Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Spectre v2: Mitigation; IBRS, IBPB conditional, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected
Srbds: Mitigation; Microcode
Tsx async abort: Mitigation; TSX disabled
```

* Operating System, e.g. for Linux:

`$ uname -a`

`Linux imixs-ai 6.1.0-18-amd64 #1 SMP PREEMPT_DYNAMIC Debian 6.1.76-1 (2024-02-01) x86_64 GNU/Linux`

* SDK version, e.g. for Linux:

```
$ python3 --version
Python 3.10.12

$ make --version
GNU Make 4.3
Built for x86_64-pc-linux-gnu
Copyright (C) 1988-2020 Free Software Foundation, Inc.
License GPLv3+: GNU GPL version 3 or later
This is free software: you are free to change and redistribute it.
There is NO WARRANTY, to the extent permitted by law.

$ g++ --version
g++ (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Copyright (C) 2021 Free Software Foundation, Inc.
This is free software; see the source for copying conditions. There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

```

# Failure Information (for bugs)

Please help provide information about the failure if this is a bug. If it is not a bug, please remove the rest of this template.

How Can I provide more useful information about the crash?